Triple
T15336100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bunkyō City |
E366668
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Sendagi district |
—
|
NE NERFINISHED |
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: Sendagi district | Statement: [Bunkyō City, contains, Sendagi district]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sendagi district Context triple: [Bunkyō City, contains, Sendagi district]
-
A.
Jujo district
Jujo district is a neighborhood in Kita Ward, Tokyo, known for its traditional shopping streets, dense residential areas, and convenient rail access.
-
B.
Nezu district
chosen
Nezu district is a historic neighborhood in Tokyo, Japan, known for its traditional atmosphere, old temples and shrines, and preserved shitamachi (downtown) charm.
-
C.
Shinsen district
Shinsen district is a neighborhood in Tokyo, Japan, known for its proximity to Shibuya and its mix of residential streets, small eateries, and nightlife spots.
-
D.
Shimen District
Shimen District is a rural coastal district in northern Taiwan known for its scenic shoreline, historic sites, and role as part of New Taipei City.
-
E.
Kanda district
Kanda district is a historic commercial and cultural area in central Tokyo known for its old bookstores, electronics shops, and traditional shrines.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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. |
Created at: April 10, 2026, 3:17 a.m.