Triple
T15336075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bunkyō City |
E366668
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Kita |
E198080
|
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: Kita | Statement: [Bunkyō City, borderedBy, Kita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kita Context triple: [Bunkyō City, borderedBy, Kita]
-
A.
Kita
chosen
Kita is one of Tokyo’s 23 special wards, located in the northern part of the city and known for its mix of residential neighborhoods, parks, and commercial areas.
-
B.
Kita Iōtō
Kita Iōtō is a remote Japanese island in the Pacific Ocean, part of the Ogasawara archipelago, known for its volcanic origin and military history.
-
C.
Kita Maninka
Kita Maninka is a regional variety of the Manding language spoken primarily around the town of Kita in western Mali.
-
D.
Kita-Senju
Kita-Senju is a major commercial and transportation hub in Tokyo, Japan, known for its busy railway station and shopping districts.
-
E.
Kitasaiwai
Kitasaiwai is a prominent commercial and business district in Nishi Ward, Yokohama, known for its offices, shopping facilities, and urban infrastructure.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e03c5f081908e4d14dbdbc7f7a6 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01f11b88819089342e8b088bc95e |
completed | May 9, 2026, 9:44 a.m. |
Created at: April 10, 2026, 3:17 a.m.