Triple
T11934361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hongo district |
E283999
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Yushima |
E295812
|
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: Yushima | Statement: [Hongo district, near, Yushima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yushima Context triple: [Hongo district, near, Yushima]
-
A.
Aobayama
Aobayama is a hilly, forested area in Sendai known for housing parts of Tohoku University and offering scenic views over the city.
-
B.
Fukusaki
Fukusaki is a town in Hyōgo Prefecture, Japan, known for its rural setting and association with folklorist Kunio Yanagita.
-
C.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
-
D.
Marunouchi
chosen
Marunouchi is a central Tokyo business district known for its concentration of corporate headquarters, upscale offices, and proximity to Tokyo Station and the Imperial Palace.
-
E.
Nagareyama
Nagareyama is a city in Chiba Prefecture, Japan, known as a residential suburb of the Tokyo metropolitan area with growing commuter access and family-oriented neighborhoods.
- 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_69d6ab2ce9c48190b5d39511b524f666 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90306fcf48190a963d2d1932288d1 |
completed | April 10, 2026, 2:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006065741c8190ad4ceb6bd3d60f9f |
completed | May 10, 2026, 10:39 a.m. |
Created at: April 8, 2026, 9:45 p.m.