Triple
T20305953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banwell |
E505608
|
entity |
| Predicate | hasPostTown |
P2711
|
FINISHED |
| Object | BANWELL |
—
|
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: BANWELL | Statement: [Banwell, hasPostTown, BANWELL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BANWELL Context triple: [Banwell, hasPostTown, BANWELL]
-
A.
Banwell
chosen
Banwell is a village and civil parish in North Somerset, England, known for its historic caves and medieval architecture.
-
B.
Bywell
Bywell is a small historic village in Northumberland, England, situated on the north bank of the River Tyne and known for its medieval churches and castle ruins.
-
C.
Whitland
Whitland is a small market town in Carmarthenshire, southwest Wales, known historically for its nearby Cistercian abbey and its role as a local agricultural and transport hub.
-
D.
Bolney
Bolney is a rural village in West Sussex, England, known for its countryside setting and local vineyards.
-
E.
Beachamwell
Beachamwell is a small rural village in Norfolk, England, known for its traditional countryside setting and historic parish church.
- 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_69e0b4b8ab648190906e18538c250148 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6773f8f688190b616f972b9bbb28e |
completed | April 20, 2026, 6:58 p.m. |
Created at: April 16, 2026, 11:18 a.m.