Triple
T1603482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinjuku Station |
E34446
|
entity |
| Predicate | locatedInWard |
P40
|
FINISHED |
| Object | Shibuya-ku |
E275204
|
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: Shibuya-ku | Statement: [Shinjuku Station, locatedInWard, Shibuya-ku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shibuya-ku Context triple: [Shinjuku Station, locatedInWard, Shibuya-ku]
-
A.
Shibuya-ku
chosen
Shibuya-ku is a major commercial and entertainment ward in central Tokyo, Japan, known for its bustling shopping districts, nightlife, and the iconic Shibuya Crossing.
-
B.
Chūō-ku
Chūō-ku is a central ward of Osaka, Japan, known as a major commercial and entertainment hub featuring famous landmarks, shopping streets, and nightlife areas.
-
C.
Meguro Ward
Meguro Ward is a residential and commercial district in southwest Tokyo known for its urban neighborhoods, cultural sites, and convenient rail access to central Tokyo.
-
D.
Setagaya
Setagaya is a large residential ward in western Tokyo, Japan, known for its suburban neighborhoods, parks, and role as a commuter area for central Tokyo.
-
E.
Shinagawa
Shinagawa is a major commercial and transportation hub in Tokyo, Japan, known for its busy railway station, business districts, and waterfront developments.
- 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_69a885fea6a481909fe83ba6441f1774 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa61df32a88190a7e823a77bdcc84a |
completed | March 6, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2f394df3c819087cd773980b42f2e |
completed | March 12, 2026, 5:10 p.m. |
Created at: March 4, 2026, 7:28 p.m.