Triple
T1823815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Wayne Assembly Plant |
E40604
|
entity |
| Predicate | employment |
P31572
|
FINISHED |
| Object | 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: thousands of workers | Statement: [Fort Wayne Assembly Plant, employment, thousands of workers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employment Context triple: [Fort Wayne Assembly Plant, employment, thousands of workers]
-
A.
employmentContext
Indicates the situational or organizational setting in which an employment relationship or work activity takes place.
-
B.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
-
C.
peakEmployment
Indicates that an entity has reached its highest level of employment or workforce size during a specified period.
-
D.
employer
Indicates a relationship where one entity hires, pays, and oversees the work of another entity.
-
E.
laborMarket
Indicates the relationship between workers seeking jobs and employers offering positions, including how wages, employment levels, and working conditions are determined through their interaction.
- 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_69a8864644bc8190b2358ab897194ac1 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab21ab83a48190a33afe5db19a21f8 |
completed | March 6, 2026, 6:49 p.m. |
| PD | Predicate disambiguation | batch_69aa61d97d008190b6642aef32eb7e36 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab21aa56108190a5123539d5020741 |
completed | March 6, 2026, 6:49 p.m. |
Created at: March 4, 2026, 7:32 p.m.