Triple
T20239069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garristown |
E498231
|
entity |
| Predicate | nearbySettlement |
P350
|
FINISHED |
| Object | Ashbourne |
—
|
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: Ashbourne | Statement: [Garristown, nearbySettlement, Ashbourne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ashbourne Context triple: [Garristown, nearbySettlement, Ashbourne]
-
A.
Ashbourne
Ashbourne is a historic market town in Derbyshire, England, known for its traditional architecture and annual Royal Shrovetide Football match.
-
B.
Ashbourne
chosen
Ashbourne is a rapidly growing commuter town in County Meath, Ireland, located northwest of Dublin and known for its residential developments and local amenities.
-
C.
Ashby
Ashby is a small rural community located within the township of Addington Highlands in eastern Ontario, Canada.
-
D.
Ashby
Ashby is a surname most notably associated with Hal Ashby, the influential American film director known for 1970s classics such as "Harold and Maude" and "Being There."
-
E.
Ashby
Ashby is a 2015 coming-of-age dramedy film in which Nat Wolff plays a high school student who befriends a retired CIA assassin, portrayed by Mickey Rourke.
- 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6716c8de88190916bfa1d6b7f79cb |
completed | April 20, 2026, 6:33 p.m. |
Created at: April 11, 2026, 11:40 p.m.