Triple
T12383296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hawes |
E295794
|
entity |
| Predicate | distanceTo |
P350
|
FINISHED |
| Object | Kendal |
E97268
|
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: Kendal | Statement: [Hawes, distanceTo, Kendal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kendal Context triple: [Hawes, distanceTo, Kendal]
-
A.
Kendal
Kendal is a regency capital and coastal town in Central Java, Indonesia, known for its agricultural economy and proximity to the provincial capital, Semarang.
-
B.
Kendal
chosen
Kendal is a historic market town in northwest England, known for its wool trade heritage, distinctive grey limestone buildings, and the mint cake associated with mountaineering.
-
C.
Kendal Green
Kendal Green is a commuter rail station in Weston, Massachusetts, served by the MBTA's Fitchburg Line.
-
D.
Biddulph
Biddulph is a small town in Staffordshire, England, known historically for its coal mining and rural surroundings.
-
E.
Kearsley
Kearsley is a town in Greater Manchester, England, historically part of Lancashire and known for its industrial heritage.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fbc3f608190b0ee3c4f304a94db |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62ac78c5c81909fb3d63bc6c9cc01 |
completed | May 2, 2026, 4:48 p.m. |
Created at: April 8, 2026, 9:54 p.m.