Triple
T513663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midtown Manhattan |
E10658
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object | Koreatown |
E3070
|
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: Koreatown | Statement: [Midtown Manhattan, hasDistrict, Koreatown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koreatown Context triple: [Midtown Manhattan, hasDistrict, Koreatown]
-
A.
Koreatown
chosen
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.
-
B.
Seoul
Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
-
C.
Gwangju
Gwangju is a major metropolitan city in southwestern South Korea known for its rich cultural heritage and pivotal role in the country’s pro-democracy movement.
-
D.
Harajuku
Harajuku is a vibrant Tokyo district famous for its youth culture, eclectic street fashion, and trendy shopping and entertainment spots.
-
E.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1804e908190a1d34ac952e84a3f |
completed | Feb. 28, 2026, 1:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4a6fbbed08190a04ced56b6b9f3d9 |
completed | March 1, 2026, 8:52 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.