Triple
T75693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LGBTPeople |
E1512
|
entity |
| Predicate | hasHealthConcern |
P4720
|
FINISHED |
| Object | barriers to gender-affirming care |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: barriers to gender-affirming care | Statement: [LGBTPeople, hasHealthConcern, barriers to gender-affirming care]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHealthConcern Context triple: [LGBTPeople, hasHealthConcern, barriers to gender-affirming care]
-
A.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
B.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
C.
remedySought
Indicates that a particular legal or corrective action is being requested as a solution or relief in response to a problem or dispute.
-
D.
hasConsequence
Indicates that one event, action, or condition leads to or results in another as its outcome or effect.
-
E.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a25314bd6c81908d1cfd4b83f20049 |
completed | Feb. 28, 2026, 2:29 a.m. |
| PD | Predicate disambiguation | batch_69a24eae77ec81909015906f31f2b62e |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a25313f1688190ba0fa8677faaf65b |
completed | Feb. 28, 2026, 2:29 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.