Triple
T7093618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Age Discrimination in Employment Act of 1967 |
E165265
|
entity |
| Predicate | employerSizeThreshold |
P46078
|
FINISHED |
| Object | 20 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: 20 or more employees | Statement: [Age Discrimination in Employment Act of 1967, employerSizeThreshold, 20 or more employees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerSizeThreshold Context triple: [Age Discrimination in Employment Act of 1967, employerSizeThreshold, 20 or more employees]
-
A.
staffSize
Indicates the number of staff members associated with an entity.
-
B.
employsApproximateNumberOfPeople
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
-
C.
employerNumberCharacteristic
chosen
Indicates a relationship where an entity is associated with a specific numeric characteristic that quantifies or identifies an employer.
-
D.
minimumNumberOfMembers
Indicates the smallest allowable or required number of members that must be present or involved in a given context or group.
-
E.
employedApproximately
Indicates that one entity employs another in a manner where the number, duration, or extent of employment is approximate rather than exact.
- 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_69c6887e8c10819091cee237560d32da |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e55159848190a794ad77e60c5525 |
completed | March 27, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c172148190bf290c07bf579d1f |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:41 p.m.