Triple
T2013672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gyeonggi Province |
E43744
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Osan |
E223589
|
NE 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: Osan | Statement: [Gyeonggi Province, hasCity, Osan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Osan Context triple: [Gyeonggi Province, hasCity, Osan]
-
A.
Osan
chosen
Osan is a city in Gyeonggi Province, South Korea, known for its proximity to Osan Air Base and its role as a regional transportation and commercial hub.
-
B.
Osaka Evessa
Osaka Evessa is a professional Japanese basketball team based in Osaka that competes in the B.League.
-
C.
Asago
Asago is a city in northern Hyōgo Prefecture, Japan, known for its mountainous scenery, historic castle ruins, and hot spring resorts.
-
D.
Tenjin
Tenjin is the Shinto kami of scholarship and learning, widely revered by students seeking academic success.
-
E.
Nerima
Nerima is a residential ward in northwestern Tokyo known for its suburban character, parks, and role as a hub for anime production studios.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a88716e9f08190946313fdc949e3cf |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb8b42d508190bf2b63132bb2ad77 |
completed | March 7, 2026, 5:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0aea65d881908eb751349a2c23f9 |
completed | March 8, 2026, 11:48 p.m. |
Created at: March 4, 2026, 7:37 p.m.