Triple
T14514355
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yeovilton |
E340478
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Yeovil |
E74538
|
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: Yeovil | Statement: [Yeovilton, near, Yeovil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yeovil Context triple: [Yeovilton, near, Yeovil]
-
A.
Yeovil
chosen
Yeovil is a town in Somerset, England, known historically for its aircraft and defence industries and serving as a commercial and service hub for the surrounding rural area.
-
B.
Torquay
Torquay is a coastal suburb of Hervey Bay in Queensland, Australia, known for its beaches and tourism amenities.
-
C.
Torquay
Torquay is a seaside resort town on the English Riviera in Devon, England, known for its mild climate, beaches, and Victorian-era tourism.
-
D.
Swindon
Swindon is a large town in Wiltshire, England, known as a major commercial and commuter hub in the southwest with strong railway and industrial heritage.
-
E.
Yeovilton
Yeovilton is a village in Somerset, England, best known for its Royal Naval Air Station and strong association with British naval aviation.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69de9a6d82988190b6f957012bcc63d4 |
completed | April 14, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6da64db881909a4f88d18031cb0c |
completed | May 8, 2026, 4:59 a.m. |
Created at: April 10, 2026, 1:21 a.m.