Triple
T3735139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lia Thomas |
E79162
|
entity |
| Predicate | hasGenderHistory |
P51355
|
FINISHED |
| Object | competed in men's swimming before transitioning |
—
|
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: competed in men's swimming before transitioning | Statement: [Lia Thomas, hasGenderHistory, competed in men's swimming before transitioning]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderHistory Context triple: [Lia Thomas, hasGenderHistory, competed in men's swimming before transitioning]
-
A.
hasIncarnationsOfGender
Indicates that an entity has different incarnations or forms that each express or are associated with a particular gender.
-
B.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
C.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
-
D.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
E.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
- 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_69ad8b0e4650819090ad7cef094285e8 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb39dc0881909cd74ff25d8c43a9 |
completed | March 8, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69adc04746588190b0dc535638f23546 |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc45debe48190b1c1f894e02b0316 |
completed | March 8, 2026, 6:47 p.m. |
Created at: March 8, 2026, 3:34 p.m.