Triple
T11524795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asiana Town |
E273263
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | 아시아나타운 |
E273263
|
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: 아시아나타운 | Statement: [Asiana Town, hasNameInLanguage, 아시아나타운]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 아시아나타운 Context triple: [Asiana Town, hasNameInLanguage, 아시아나타운]
-
A.
Koreatown
Koreatown is a dense Los Angeles neighborhood known for its vibrant Korean-American community, late-night dining, and mix of historic and modern urban development.
-
B.
Koreatown
Koreatown is a vibrant Manhattan neighborhood known for its dense concentration of Korean restaurants, shops, and cultural businesses centered around West 32nd Street near the Empire State Building.
-
C.
Asiana Town
chosen
Asiana Town is the main corporate headquarters complex of South Korea’s Asiana Airlines in Seoul.
-
D.
China Town
"China Town" is a 1962 Hindi-language Indian crime thriller film starring Shammi Kapoor in a double role, known for its blend of suspense, music, and drama.
-
E.
China Town
China Town was the original name of the Nevada settlement that later became the town of Dayton.
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d87fd26648819083de19bcddf8ad69 |
completed | April 10, 2026, 4:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e62562efb88190bbf3c7bbec8233aa |
completed | April 20, 2026, 1:08 p.m. |
Created at: April 8, 2026, 9:37 p.m.