Triple
T20644594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sir Ifor Williams |
E507319
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Bangor, Wales |
—
|
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: Bangor, Wales | Statement: [Sir Ifor Williams, workLocation, Bangor, Wales]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bangor, Wales Context triple: [Sir Ifor Williams, workLocation, Bangor, Wales]
-
A.
Bangor
Bangor is a coastal commune located on Belle-Île, an island off the coast of Brittany in northwestern France.
-
B.
Bangor
chosen
Bangor is a historic cathedral city in northwest Wales, known for its university and scenic location near the Menai Strait.
-
C.
Bangor
Bangor is a small borough in eastern Pennsylvania known historically for its slate quarrying industry and close-knit community.
-
D.
Bangor
Bangor is a coastal town in Northern Ireland known for its marina, seaside resort heritage, and role as a commuter hub for nearby Belfast.
-
E.
Caernarfon, Gwynedd
Caernarfon, Gwynedd is a historic town in northwest Wales famed for its medieval castle and strong royal connections.
- 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_69e0b4be702c8190a3d2410a881d310a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6af1d0be481909e090193dcfd9cf6 |
completed | April 20, 2026, 10:56 p.m. |
Created at: April 16, 2026, 11:43 a.m.