Triple
T17964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manhattan Project |
E354
|
entity |
| Predicate | employedPeople |
P1211
|
FINISHED |
| Object | over 130000 |
—
|
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: over 130000 | Statement: [Manhattan Project, employedPeople, over 130000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employedPeople Context triple: [Manhattan Project, employedPeople, over 130000]
-
A.
employer
Indicates a relationship where one entity hires, pays, and oversees the work of another entity.
-
B.
worksWith
Indicates that two entities collaborate or perform tasks together in a shared work-related context.
-
C.
staffIncluded
Indicates that staff members are included or provided as part of the associated entity, service, or arrangement.
-
D.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
E.
hasAlumni
Indicates that an institution or organization is associated with individuals who formerly attended or graduated from it.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a242494a548190a5776fb6cad4d4af |
completed | Feb. 28, 2026, 1:18 a.m. |
| PD | Predicate disambiguation | batch_69a23fedf0fc8190ad99bd1da297b14d |
completed | Feb. 28, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69a242489dbc819092c100d3fbf130ef |
completed | Feb. 28, 2026, 1:18 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.