Triple
T13012432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nezu |
E322452
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Sendagi |
E208887
|
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: Sendagi | Statement: [Nezu, adjacentTo, Sendagi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sendagi Context triple: [Nezu, adjacentTo, Sendagi]
-
A.
Sendagi
chosen
Sendagi is a traditional residential neighborhood in Tokyo known for its preserved shitamachi atmosphere, narrow streets, and historic temples and shops.
-
B.
Sendagaya
Sendagaya is a neighborhood in Tokyo known for its sports facilities, including the National Stadium, and its proximity to Shinjuku and Harajuku.
-
C.
Modogashe
Modogashe is a small, remote town in northeastern Kenya known as a local trading and transit center in a semi-arid pastoral region.
-
D.
Asago
Asago is a city in northern Hyōgo Prefecture, Japan, known for its mountainous scenery, historic castle ruins, and hot spring resorts.
-
E.
Asokoro
Asokoro is an upscale residential and administrative district in Abuja, Nigeria, known for hosting many government institutions, embassies, and high-profile residents.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecbb8f4819094d55eb07cb5ad97 |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbc77f308190b3b47f7a092db434 |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:49 p.m.