Triple
T8002944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Epsom College |
E186294
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Epsom |
E24540
|
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: Epsom | Statement: [Epsom College, locatedIn, Epsom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Epsom Context triple: [Epsom College, locatedIn, Epsom]
-
A.
Epsom
chosen
Epsom is a market town in Surrey, England, best known for the Epsom Downs Racecourse and the famous Epsom Derby horse race.
-
B.
Yate
Yate is a town in South Gloucestershire, England, known as a residential and commercial centre north of Bristol.
-
C.
Bursley
Bursley is a fictional Staffordshire pottery town created by Arnold Bennett as one of his "Five Towns" and used as a principal setting in several of his novels.
-
D.
Leamington Spa
Leamington Spa is a historic spa town in Warwickshire, England, known for its Regency architecture, landscaped parks, and former popularity as a fashionable health resort.
-
E.
Farnham
Farnham is a historic market town in southern England known for its Georgian streets, medieval castle, and surrounding Surrey countryside.
- 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_69ca82aaaf24819084b94d18f699ba53 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3cf2918081909ee0afab11caed63 |
completed | March 31, 2026, 3:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe121b0ac81908b9da58cf14c8df5 |
completed | March 31, 2026, 2:58 p.m. |
Created at: March 30, 2026, 5:18 p.m.