Triple
T23395339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berar region |
E559341
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object | Washim |
—
|
NE NERFINISHED |
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: Washim | Statement: [Berar region, majorCity, Washim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Washim Context triple: [Berar region, majorCity, Washim]
-
A.
Washim
chosen
Washim is a city in the Vidarbha region of Maharashtra, India, known as an important local administrative and commercial center.
-
B.
Uwajima
Uwajima is a coastal city in southwestern Shikoku, Japan, known for its historic castle, fishing industry, and traditional bullfighting events.
-
C.
Inabe
Inabe is a city in Mie Prefecture, Japan, known for its rural landscapes, agriculture, and access to regional transport routes.
-
D.
Nagiso
Nagiso is a town in Nagano Prefecture, Japan, known as a traditional post-town area along the historic Nakasendō route in the Kiso Valley.
-
E.
Tsuyama
Tsuyama is a historic castle town in Okayama Prefecture, Japan, known for its well-preserved samurai district, cherry blossoms, and former Tsuyama Castle ruins.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e24549610c8190a069d6411ce5f661 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a4db1c888190ace5d58bcc8645c1 |
completed | April 29, 2026, 6:27 a.m. |
Created at: April 17, 2026, 5:36 p.m.