Triple
T1440493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Price Waterhouse v. Hopkins |
E31057
|
entity |
| Predicate | genderStereotypingRecognizedAs |
P29340
|
FINISHED |
| Object | evidence of sex discrimination |
—
|
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: evidence of sex discrimination | Statement: [Price Waterhouse v. Hopkins, genderStereotypingRecognizedAs, evidence of sex discrimination]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderStereotypingRecognizedAs Context triple: [Price Waterhouse v. Hopkins, genderStereotypingRecognizedAs, evidence of sex discrimination]
-
A.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
B.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
C.
genderDivision
Indicates a relationship where roles, responsibilities, or categories are separated or distinguished based on gender.
-
D.
genderPersonification
Indicates that a non-human entity is represented or treated as having a specific gender.
-
E.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another 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_69a4991633388190a4d61b5a98aa407a |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5ff8dbc81909eafcfc9f2260a22 |
completed | March 1, 2026, 11:04 p.m. |
| PD | Predicate disambiguation | batch_69a4c478f65481909ee716791c663491 |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c5fd2c5c81909283b7a74aff89b7 |
completed | March 1, 2026, 11:04 p.m. |
Created at: March 1, 2026, 8 p.m.