Triple
T12845582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arakawa |
E307166
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Kita |
E198080
|
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: Kita | Statement: [Arakawa, borders, Kita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kita Context triple: [Arakawa, borders, Kita]
-
A.
Kita
chosen
Kita is one of Tokyo’s 23 special wards, located in the northern part of the city and known for its mix of residential neighborhoods, parks, and commercial areas.
-
B.
Kita Iōtō
Kita Iōtō is a remote Japanese island in the Pacific Ocean, part of the Ogasawara archipelago, known for its volcanic origin and military history.
-
C.
Kita-Senju
Kita-Senju is a major commercial and transportation hub in Tokyo, Japan, known for its busy railway station and shopping districts.
-
D.
Kitasaiwai
Kitasaiwai is a prominent commercial and business district in Nishi Ward, Yokohama, known for its offices, shopping facilities, and urban infrastructure.
-
E.
Kiblawan
Kiblawan is a rural municipality in the province of Davao del Sur in the Philippines, known for its agricultural economy and upland communities.
- 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_69d7bdf5e7cc8190be357278bc5ba3bb |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96ff49efc8190bd6bbac510cc4705 |
completed | April 10, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69b9fa40c8190bbc2c6ad22795de4 |
completed | May 3, 2026, 12:49 a.m. |
Created at: April 9, 2026, 5:36 p.m.