Triple
T16790071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warwick Castle |
E408081
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Warwick |
E146701
|
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: Warwick | Statement: [Warwick Castle, locatedIn, Warwick]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Warwick Context triple: [Warwick Castle, locatedIn, Warwick]
-
A.
Warwick
Warwick is a historic coastal city in Rhode Island known for its role in early American colonial history and as a major suburb of Providence.
-
B.
Warwick
chosen
Warwick is a historic market town in the English Midlands, renowned for its medieval architecture and the prominent Warwick Castle.
-
C.
Warwick
Warwick is a regional city in Queensland, Australia, known as an agricultural and service hub on the Southern Downs.
-
D.
Warwick
Warwick is a surname of English origin borne by various notable individuals and families, including members of the Drinkard and Warwick musical families.
-
E.
Warwick
Warwick is a central character in the 1990 horror film "Graveyard Shift," known for his role in the sinister events that unfold in the movie's haunted textile mill.
- 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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2a50e18819090a30e1f38e520e0 |
completed | April 18, 2026, 4:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b28970748190836806c68ad9e230 |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:22 a.m.