Triple
T1440347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pregnancy Discrimination Act of 1978 |
E31054
|
entity |
| Predicate | appliesToEmployersWith |
P1129
|
FINISHED |
| Object | 15 or more employees |
—
|
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: 15 or more employees | Statement: [Pregnancy Discrimination Act of 1978, appliesToEmployersWith, 15 or more employees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToEmployersWith Context triple: [Pregnancy Discrimination Act of 1978, appliesToEmployersWith, 15 or more employees]
-
A.
employersInclude
Indicates that a specified set or group of employers contains, as members, the employer or employers referenced by the other argument.
-
B.
namedForEmployer
Indicates that an entity is named after, or in honor of, its employer.
-
C.
appliesTo
chosen
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
D.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
E.
appliesToPerson
Indicates that something (such as a rule, condition, or attribute) is relevant or applicable to a specific person.
- F. None of above.
Provenance (3 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. |
Created at: March 1, 2026, 8 p.m.