Triple
T1302036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Incheon |
E27787
|
entity |
| Predicate | administrativeCountyCount |
P1679
|
FINISHED |
| Object | 2 counties |
—
|
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: 2 counties | Statement: [Incheon, administrativeCountyCount, 2 counties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: administrativeCountyCount Context triple: [Incheon, administrativeCountyCount, 2 counties]
-
A.
numberOfDistricts
chosen
Indicates the total count of districts associated with a given entity or area.
-
B.
numberOfRegionalCouncils
Indicates the total count of regional councils associated with a given entity.
-
C.
includesCounty
Indicates that a larger geographic or administrative region contains or encompasses a specific county within its boundaries.
-
D.
hasCountyAdministrator
Indicates that an entity is administered or overseen by a specific county administrator.
-
E.
hasCountyCode
Indicates that an entity is associated with a specific county identified by a standardized county code.
- 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_69a496d6682881909ba658f1c1e0e2b0 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c115ba64819081c55fa6807e19ef |
completed | March 1, 2026, 10:43 p.m. |
| PD | Predicate disambiguation | batch_69a4bee8544c8190874efd9bae9bccf9 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.