Triple
T193481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Recovery Administration |
E3768
|
entity |
| Predicate | employerRequirement |
P100
|
FINISHED |
| Object | adopt codes of fair competition |
—
|
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: adopt codes of fair competition | Statement: [National Recovery Administration, employerRequirement, adopt codes of fair competition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerRequirement Context triple: [National Recovery Administration, employerRequirement, adopt codes of fair competition]
-
A.
eligibilityCriteria
Indicates the conditions or requirements that must be satisfied for an entity to qualify for or be considered eligible for something.
-
B.
requires
chosen
Indicates that one entity must exist, occur, or be satisfied before another entity can exist, occur, or be carried out.
-
C.
employedPeople
Indicates that there exists a relationship where people are currently working in jobs or positions, typically under an employer.
-
D.
eligibility
Indicates that an entity meets the required conditions or qualifications to participate in, receive, or perform something.
-
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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a2596810c48190ab687c0c2efaa9e2 |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a256769ad8819083c1d83082c0215e |
completed | Feb. 28, 2026, 2:44 a.m. |
Created at: Feb. 28, 2026, 2:41 a.m.