OpenAI GPT-5.6 Sol
Used for structured data extraction and graph resolution.
According to the Acu-Cell Nutrition findings, the physiological effects of boron supplementation on steroid hormones involve significant regulatory interactions rather than a simple one-way increase.
Boron supplementation has been shown in clinical studies to increase blood levels of 17-beta estradiol (the primary form of estrogen) in postmenopausal women and to a lesser extent in men.
Rather than directly stimulating the production of hormones, boron primarily acts by reducing the rate of elimination (excretion) of steroid hormones, allowing them to remain active in the bloodstream longer.
The metabolic impact of boron is closely tied to other trace minerals—most notably calcium, magnesium, and copper. Altering one often shifts the tissue balance and utilization of the others.
Excessive boron intake can lead to mineral imbalances or unwanted hormonal shifts, as optimal physiological function relies strictly on homeostatic balance.
Response to boron depends heavily on an individual's baseline mineral status, age, and existing endocrine function.
Boron ->
-> Boron
Boron supplementation ->
-> Plasma estradiol
A source-grounded pipeline turns Acu-Cell pages into a map of entities and claims. The graph organizes what the source says; it does not invent a medical answer or certify that a claim is correct.
The source pages are converted into clean, ordered text. Section context is retained so a sentence is interpreted with the heading and surrounding material that give it meaning.
A schema-constrained language model reads one complete source record at a time and identifies entities, directed relationships, endpoint roles, qualifiers and a rationale. The extraction rules prohibit creating a relationship from proximity or formatting alone.
Dose, timing, certainty, evidence and other conditions remain qualifiers on the precise relationship they modify. General facts about an entity become attributes. This prevents a qualified statement from being displayed as an unconditional one.
Name similarity, shared attributes, explicit identity statements and mutual semantic similarity produce 784 candidate groups. This stage only nominates candidates; it cannot merge them.
Each candidate group is judged against its source evidence. Names merge only when the supplied records establish exact identity, not merely because the terms seem related. The build merged 193 groups and absorbed 209 duplicate entity records; uncertain cases remain separate.
Relationship names are combined only when they are interchangeable. Broader and narrower meanings stay distinct. Resolved entity names are then applied to every claim, duplicate edges are consolidated without erasing qualifier differences, and the searchable graph is produced.
Used for structured data extraction and graph resolution.
Used under human direction to consolidate duplicate entities, validate the extracted data, and correct errors.
The source states that high levels of calcium can slow healing.
The condition stays on the relationship. It does not become a separate entity called “high calcium,” and it is not discarded.
The graph is an automated interpretation of source material, not an independent scientific review. Extraction can miss context, choose the wrong direction, overstate a relationship or fail to recognize two names as the same thing. The source itself may also be incomplete, disputed or outdated. Use the rationale and source links to inspect important claims directly.