Triple
T8781011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shibuya |
E208724
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Hatagaya |
E30889
|
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: Hatagaya | Statement: [Shibuya, contains, Hatagaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hatagaya Context triple: [Shibuya, contains, Hatagaya]
-
A.
Hatagaya
chosen
Hatagaya is a residential neighborhood in Tokyo known for its convenient access to central Shibuya and its mix of quiet local streets and urban amenities.
-
B.
Hiranaka
Hiranaka is a Japanese surname borne by individuals such as former professional boxer Akinobu Hiranaka.
-
C.
Nagahori
Nagahori is a district in Osaka, Japan, known primarily as an urban area served by the Osaka Metro Nagahori Tsurumi-ryokuchi Line.
-
D.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
E.
Hatano
Hatano was a former municipality in Niigata Prefecture, Japan, that was incorporated into the city of Sado through a merger.
- 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_69ca835fbee88190bf625939bac48d7f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f55b7b08190ab3e18cd634a144b |
completed | March 31, 2026, 11:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d5434600c48190b584b6e791c4f3d6 |
completed | April 7, 2026, 5:47 p.m. |
Created at: March 30, 2026, 6:42 p.m.