Triple
T29206053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yeongnam |
E740413
|
entity |
| Predicate | hasMajorIndustrialCity |
P157920
|
FINISHED |
| Object | Ulsan |
—
|
NE NERFINISHED |
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: Ulsan | Statement: [Yeongnam, hasMajorIndustrialCity, Ulsan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMajorIndustrialCity Context triple: [Yeongnam, hasMajorIndustrialCity, Ulsan]
-
A.
hasMajorCity
Indicates that a location possesses at least one city of significant size, importance, or influence within its region or country.
-
B.
hasMajorCityFunction
Indicates that an entity serves an important urban role or function typically associated with a major city (such as being a commercial, administrative, or cultural center).
-
C.
hasIndustrialTown
Indicates that an entity possesses or is associated with a town characterized primarily by industrial activities or facilities.
-
D.
hasMajorCityOfUse
Indicates that a particular city is the primary or most significant location where something (e.g., a product, language, service) is predominantly used or applied.
-
E.
isUrbanAndIndustrialHub
chosen
Indicates that a place functions as a major center of urban activity and industrial production.
- 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_69f07cb974108190b7e86ca489a6ebb6 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69ff255b84788190a94682f4efe1d0b8 |
completed | May 9, 2026, 12:15 p.m. |
| PD | Predicate disambiguation | batch_69ff24f3ab108190bb017a656cff3d82 |
completed | May 9, 2026, 12:13 p.m. |
Created at: April 28, 2026, 12:09 p.m.