Triple
T1655529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geumjeong District |
E35789
|
entity |
| Predicate | administrativeDivisions |
P10770
|
FINISHED |
| Object | multiple dongs |
—
|
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: multiple dongs | Statement: [Geumjeong District, administrativeDivisions, multiple dongs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: administrativeDivisions Context triple: [Geumjeong District, administrativeDivisions, multiple dongs]
-
A.
politicalDivision
chosen
Indicates that one entity is a governmental or administrative subdivision or jurisdiction within the territory or authority of another entity.
-
B.
countrySubdivision
Indicates that one geopolitical region is an administrative or territorial subdivision of a larger country.
-
C.
countrySubdivisionType
Indicates the specific type or category of an administrative or territorial subdivision within a country (e.g., state, province, region).
-
D.
countrySubdivisionStandardLink
Indicates a reference or link to the standard or authoritative specification that defines the country’s internal subdivisions.
-
E.
arealRegion
Indicates that something occupies or pertains to a specific two-dimensional geographic or spatial area.
- 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_69a8860568888190a32cd9f70acbba42 |
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_69a907cff53c8190b424f088478d3e2c |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:29 p.m.