Triple
T11763776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arfon |
E279725
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Caernarfon |
E181091
|
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: Caernarfon | Statement: [Arfon, contains, Caernarfon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caernarfon Context triple: [Arfon, contains, Caernarfon]
-
A.
Caernarfon, Gwynedd
chosen
Caernarfon, Gwynedd is a historic town in northwest Wales famed for its medieval castle and strong royal connections.
-
B.
Bodelwyddan
Bodelwyddan is a village in Denbighshire, North Wales, known for its strategic location near the A55 and landmarks such as Bodelwyddan Castle.
-
C.
Barmouth
Barmouth is a seaside town and popular holiday resort on the west coast of Wales, known for its sandy beach and scenic location at the mouth of the Mawddach Estuary.
-
D.
Tywyn
Tywyn is a coastal town in Gwynedd, Wales, known for its sandy beaches on Cardigan Bay and proximity to the scenic Talyllyn Railway.
-
E.
Ruthin
Ruthin is a historic market town in Denbighshire, north Wales, known for its medieval architecture and hilltop location in the Vale of Clwyd.
- 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_69d6ab01d2688190ad8ed6bda487eaa5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a5248e0881909ed1b4df7be422f7 |
completed | April 10, 2026, 7:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6717a5ca081908bdba9b944b76258 |
completed | May 2, 2026, 9:49 p.m. |
Created at: April 8, 2026, 9:41 p.m.