Triple
T1514428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Point, Georgia |
E32086
|
entity |
| Predicate | regionalEconomy |
P16022
|
FINISHED |
| Object | automotive manufacturing |
—
|
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: automotive manufacturing | Statement: [West Point, Georgia, regionalEconomy, automotive manufacturing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalEconomy Context triple: [West Point, Georgia, regionalEconomy, automotive manufacturing]
-
A.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
B.
economicCommunity
Indicates a relationship where entities are linked through shared economic integration, cooperation, or common market arrangements forming an economic community.
-
C.
economicSectorSourceOfWealth
chosen
Indicates that a particular economic sector is the primary source from which an entity derives its wealth or income.
-
D.
hasMajorEconomicRegion
Indicates that an entity includes, is associated with, or is part of a primary or significant economic region within a larger economic or geographic context.
-
E.
economicFunction
Indicates the role or purpose an entity serves within an economic system, such as how it contributes to production, distribution, or consumption of goods and services.
- 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_69a885e8caf88190a5fbb6159ce87786 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9396e16408190b5e7b0ac43376d81 |
completed | March 5, 2026, 8:06 a.m. |
| PD | Predicate disambiguation | batch_69a907aa67cc81909f00135365447399 |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:26 p.m.