Triple
T18638461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ripon Cathedral |
E455617
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Ripon |
—
|
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: Ripon | Statement: [Ripon Cathedral, locatedIn, Ripon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ripon Context triple: [Ripon Cathedral, locatedIn, Ripon]
-
A.
Ripon
Ripon is a small city in California’s Central Valley known for its agricultural roots and tight-knit community.
-
B.
Ripon
chosen
Ripon is a historic cathedral city in North Yorkshire, England, known for Ripon Cathedral and its traditional hornblower ceremony.
-
C.
City of York
The City of York is a historic cathedral city in North Yorkshire, England, renowned for its medieval walls, Gothic York Minster, and well-preserved old town.
-
D.
Chester
Chester is a historic walled city in northwest England known for its Roman heritage, medieval architecture, and distinctive two-tiered shopping galleries called the Rows.
-
E.
Chester
Chester is a historic walled city in northwest England, renowned for its well-preserved Roman and medieval 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fcac2d48190a15ce8cc8e175198 |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 10, 2026, 11:47 a.m.