Triple
T8899592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graveley |
E211894
|
entity |
| Predicate | civilParish |
P2739
|
FINISHED |
| Object | Graveley |
E211894
|
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: Graveley | Statement: [Graveley, civilParish, Graveley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Graveley Context triple: [Graveley, civilParish, Graveley]
-
A.
Graveley
chosen
Graveley is a small village and civil parish in the county of Hertfordshire in England.
-
B.
Grayling
Grayling is a small city in northern Michigan known as a gateway to outdoor recreation in the surrounding forests, rivers, and lakes.
-
C.
Leagrave
Leagrave is a suburban area of Luton in Bedfordshire, England, known as the district where the River Lea originates.
-
D.
Glazebury
Glazebury is a village in the borough of Warrington, England, situated near Culcheth and known for its semi-rural character and local community amenities.
-
E.
Brimley
Brimley is a surname most notably associated with American actor Wilford Brimley, known for his roles in film, television, and commercials.
- 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_69ca83918d3081909b326fa3750cb8c8 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc64278b208190afc3dec64ecdb0f5 |
completed | April 1, 2026, 12:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfac10be588190bd1b44f09ded7826 |
completed | April 3, 2026, 12:01 p.m. |
Created at: March 30, 2026, 6:54 p.m.