Triple
T1854715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Motors Flint Assembly Plant |
E41674
|
entity |
| Predicate | employerIn |
P33603
|
FINISHED |
| Object | Flint labor market |
—
|
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: Flint labor market | Statement: [General Motors Flint Assembly Plant, employerIn, Flint labor market]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerIn Context triple: [General Motors Flint Assembly Plant, employerIn, Flint labor market]
-
A.
employer
Indicates a relationship where one entity hires, pays, and oversees the work of another entity.
-
B.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
C.
formerEmployer
Indicates that one entity previously employed the other but no longer does so.
-
D.
collegeEmployer
Indicates that a college or university is the employing institution of a given person or organization.
-
E.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
- 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_69a8864a83848190a4ec02721306c511 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb231de14819091da3a20ed03c430 |
completed | March 7, 2026, 5:05 a.m. |
| PD | Predicate disambiguation | batch_69abafde4598819099d8229128348fd3 |
completed | March 7, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69abb22f36f08190abf5e295ddf310d7 |
completed | March 7, 2026, 5:05 a.m. |
Created at: March 4, 2026, 7:33 p.m.