Triple
T2151604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lilly Ledbetter Fair Pay Act of 2009 |
E47192
|
entity |
| Predicate | coversGrounds |
P36265
|
FINISHED |
| Object | sex |
—
|
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: sex | Statement: [Lilly Ledbetter Fair Pay Act of 2009, coversGrounds, sex]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coversGrounds Context triple: [Lilly Ledbetter Fair Pay Act of 2009, coversGrounds, sex]
-
A.
hasGrounds
Indicates that one entity possesses or includes a physical area of land or outdoor space associated with it.
-
B.
listsGrounds
Indicates that one entity enumerates or specifies the reasons, bases, or justifications (grounds) associated with another entity.
-
C.
fieldCovered
Indicates that a specified field or area is physically or functionally covered by some material, object, or condition.
-
D.
ground
Indicates that one entity is in contact with or supported by the ground or a ground-like surface.
-
E.
hasSportsGround
Indicates that an entity possesses, includes, or is associated with a sports ground or athletic field as part of its facilities or area.
- 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_69a88a1933e0819094f18426ed74180f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe4747a0819080e2234f3ea8995f |
completed | March 7, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69abbd9a60648190b20b116be5c7ad98 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbe4252688190944491a450383450 |
completed | March 7, 2026, 5:57 a.m. |
Created at: March 4, 2026, 7:44 p.m.