Triple
T14753125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cam and Dursley railway station |
E346660
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Cam |
E798003
|
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: Cam | Statement: [Cam and Dursley railway station, locatedNear, Cam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cam Context triple: [Cam and Dursley railway station, locatedNear, Cam]
-
A.
Cam
chosen
Cam is a village in Gloucestershire, England, situated near the Wildfowl and Wetlands Trust site at Slimbridge along the River Severn.
-
B.
Cam
Cam is a 2018 psychological horror-thriller film about a camgirl whose online identity is mysteriously stolen by a sinister doppelgänger.
-
C.
Cam
Cam is the given name of Cam Neely, a former professional ice hockey player and current president of the Boston Bruins.
-
D.
Com
Com is a small coastal village in eastern Timor-Leste known for its beaches, fishing community, and growing eco-tourism.
-
E.
Cel
Cel is an Etruscan goddess associated with the earth and the underworld in ancient Italic religion.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7d40efc8190bb1be34c19a2b57c |
completed | April 14, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0cea5d348190a84970da131292ee |
completed | May 8, 2026, 4:18 p.m. |
Created at: April 10, 2026, 1:30 a.m.