Triple
T3530367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinagawa |
E74645
|
entity |
| Predicate | hasEnglishName |
P3437
|
FINISHED |
| Object | Shinagawa City |
E74645
|
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: Shinagawa City | Statement: [Shinagawa, hasEnglishName, Shinagawa City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shinagawa City Context triple: [Shinagawa, hasEnglishName, Shinagawa City]
-
A.
Shibuya City
Shibuya City is a major commercial and entertainment district in central Tokyo, Japan, famous for its bustling scramble crossing, youth culture, and fashion scene.
-
B.
Bunkyō City
Bunkyō City is a special ward in central Tokyo, Japan, known for its universities, historic temples, and quiet residential neighborhoods.
-
C.
Sumida City
Sumida City is a special ward of Tokyo, Japan, known for landmarks such as the Tokyo Skytree and its traditional downtown neighborhoods.
-
D.
Hachiōji
Hachiōji is a city in western Tokyo, Japan, known as a regional commercial and educational hub with rich historical sites and access to nearby mountains and nature.
-
E.
Shinagawa
chosen
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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc9764a881908aa8d25dc9adf59e |
completed | March 8, 2026, 6:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfc60426248190947e1c58fe4dd1b1 |
completed | March 22, 2026, 10:35 a.m. |
Created at: March 8, 2026, 3:19 p.m.