Triple
T8525479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nam-gu |
E201803
|
entity |
| Predicate | hasNotableIndustryNearby |
P20649
|
FINISHED |
| Object | automobile 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: automobile manufacturing | Statement: [Nam-gu, hasNotableIndustryNearby, automobile manufacturing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableIndustryNearby Context triple: [Nam-gu, hasNotableIndustryNearby, automobile manufacturing]
-
A.
hasNearbyIndustry
chosen
Indicates that an entity is located close to one or more industrial facilities or activities.
-
B.
hasMajorCompanyNearby
Indicates that a location or entity is situated close to at least one large or significant company.
-
C.
nearbyEconomicActivity
Indicates that there is economic activity occurring in close physical proximity to the referenced entity.
-
D.
hasNearbyDevelopment
Indicates that a development or construction project exists in close physical proximity to the referenced entity.
-
E.
hasNotableCompanyHeadquarters
Indicates that a company’s headquarters is recognized as notable or significant in some way.
- 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_69ca83228b24819085d22e7dc99f5d94 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe644c4648190a14dcaeaa90d72c7 |
completed | March 31, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cbd10f64b4819080859057c19e58f0 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:16 p.m.