Triple
T9293874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osan |
E223589
|
entity |
| Predicate | countryCode |
P208
|
FINISHED |
| Object | KR |
E577393
|
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: KR | Statement: [Osan, countryCode, KR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KR Context triple: [Osan, countryCode, KR]
-
A.
KR
KR is the vehicle registration code used on license plates for the German city of Krefeld.
-
B.
KR
KR is the stock ticker symbol for The Kroger Co., one of the largest supermarket chains in the United States.
-
C.
KER
KER is the stock ticker symbol for Kering, the French multinational luxury goods group that owns brands such as Gucci, Saint Laurent, and Bottega Veneta.
-
D.
KOR
KOR is the FIFA country code representing the South Korea national football team in international competitions.
-
E.
.kr
chosen
.kr is the country code top-level domain (ccTLD) designated for websites associated with South Korea.
- 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_69ca8423edb08190bc0c91287a484768 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd089a2ed0819086b2e4219ff2453e |
completed | April 1, 2026, 11:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0b241251c81909aa4e8bcf5cd9c2e |
completed | April 4, 2026, 6:40 a.m. |
Created at: March 30, 2026, 7:35 p.m.