Triple
T7860677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dong-gu, Ulsan |
E182488
|
entity |
| Predicate | hasIndustrialFacilities |
P25392
|
FINISHED |
| Object | shipbuilding industry facilities |
—
|
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: shipbuilding industry facilities | Statement: [Dong-gu, Ulsan, hasIndustrialFacilities, shipbuilding industry facilities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIndustrialFacilities Context triple: [Dong-gu, Ulsan, hasIndustrialFacilities, shipbuilding industry facilities]
-
A.
hasIndustrialPlant
chosen
Indicates that an entity possesses, operates, or is associated with an industrial plant facility.
-
B.
hasIndustrialTown
Indicates that an entity possesses or is associated with a town characterized primarily by industrial activities or facilities.
-
C.
hasIndustrialPark
Indicates that a location or entity possesses or contains an industrial park within its area or jurisdiction.
-
D.
hasIndustrialCompany
Indicates that one entity possesses, controls, or is associated with an industrial company.
-
E.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
- 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_69ca82887fd48190975896bf38c4596b |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb36bcb5cc8190a8a384ce0f020b9f |
completed | March 31, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69cae925ca388190ae4a01fa76e957e8 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:53 p.m.