Triple
T7824423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lumine department store |
E181210
|
entity |
| Predicate | hasLocation |
P40
|
FINISHED |
| Object | Omiya |
E250535
|
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: Omiya | Statement: [Lumine department store, hasLocation, Omiya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Omiya Context triple: [Lumine department store, hasLocation, Omiya]
-
A.
Omiya
chosen
Omiya is a major commercial and transportation hub in Saitama Prefecture, Japan, known for its busy railway station and urban center.
-
B.
Utsunomiya
Utsunomiya is a city in Tochigi Prefecture, Japan, known as a regional commercial center and for its specialty gyoza (dumplings).
-
C.
Kisarazu
Kisarazu is a coastal city in Chiba Prefecture, Japan, known as the mainland terminus of the Tokyo Bay Aqua-Line expressway.
-
D.
Ōgaki
Ōgaki is a former municipality in Hiroshima Prefecture, Japan, that was incorporated into the city of Etajima.
-
E.
Ichinomiya
Ichinomiya is a city in Aichi Prefecture, Japan, known historically as a textile and commercial center within the Nagoya metropolitan area.
- 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_69ca8282ccec819083c48efb72d21cf9 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cafa0c1f5c8190b16db20daad159a1 |
completed | March 30, 2026, 10:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58a50d0748190a429af33cdced80a |
completed | April 20, 2026, 2:07 a.m. |
Created at: March 30, 2026, 4:42 p.m.