Triple
T270066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clinton Engineer Works |
E5611
|
entity |
| Predicate | hadWorkforce |
P803
|
FINISHED |
| Object | tens of thousands of workers |
—
|
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: tens of thousands of workers | Statement: [Clinton Engineer Works, hadWorkforce, tens of thousands of workers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadWorkforce Context triple: [Clinton Engineer Works, hadWorkforce, tens of thousands of workers]
-
A.
workedAs
Indicates that an entity held a particular job, role, or position, performing work in that capacity.
-
B.
workedUnder
Indicates that one entity was hierarchically subordinate to and performed work under the supervision or authority of another entity.
-
C.
involvesWorkers
Indicates that an event, process, or situation includes workers as active participants or affected parties.
-
D.
employedPeople
Indicates that there exists a relationship where people are currently working in jobs or positions, typically under an employer.
-
E.
employedApproximately
chosen
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_69a25853594c8190b05ec3a586ec88bf |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25e69a9248190b9e7959b43223baa |
completed | Feb. 28, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69a25b721180819080d43c43fcbccf87 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:57 a.m.