Triple
T17506615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Exeter Hospital |
E426333
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Exeter |
—
|
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: Exeter | Statement: [Exeter Hospital, city, Exeter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Exeter Context triple: [Exeter Hospital, city, Exeter]
-
A.
Exeter
chosen
Exeter is a historic cathedral city in Devon, England, known for its medieval architecture and role as a regional administrative and cultural center.
-
B.
Exeter
Exeter is a historic town in Rockingham County, New Hampshire, known for its colonial heritage and as the home of the prestigious Phillips Exeter Academy.
-
C.
Exeter
Exeter is a small borough in Luzerne County, Pennsylvania, situated in the Wyoming Valley near the Susquehanna River.
-
D.
Exeter
Exeter is a small town located in Otsego County in central New York State, known for its rural character and agricultural landscape.
-
E.
Exeter
Exeter is a small village in the Southern Highlands of New South Wales, Australia, known for its rural charm, cool climate, and English-style gardens and architecture.
- 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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e45258b73c81909db581d4f1d27921 |
completed | April 19, 2026, 3:56 a.m. |
Created at: April 10, 2026, 5:48 a.m.