Triple
T1691121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gijang County |
E36549
|
entity |
| Predicate | hasLocalSpecialty |
P17971
|
FINISHED |
| Object | anchovies |
—
|
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: anchovies | Statement: [Gijang County, hasLocalSpecialty, anchovies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalSpecialty Context triple: [Gijang County, hasLocalSpecialty, anchovies]
-
A.
hasSpecialtyFood
chosen
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
B.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
C.
specialMunicipality
Indicates that an entity is designated as a special municipality, typically having a distinct administrative or legal status compared to regular municipalities.
-
D.
regionSpecialization
Indicates that a region is designated or recognized as being particularly focused on, adapted to, or specialized in a specific function, activity, or domain.
-
E.
hasSpecialUnit
Indicates that an entity possesses or is associated with a distinct, designated unit that has a special role, function, or status.
- 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf3359ce48190803b322db8ad6027 |
completed | March 6, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69aa61b71cec8190b273588051058ebd |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.