Triple
T8360918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Filey |
E197003
|
entity |
| Predicate | postalTown |
P2711
|
FINISHED |
| Object | FILEY |
E197003
|
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: FILEY | Statement: [Filey, postalTown, FILEY]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FILEY Context triple: [Filey, postalTown, FILEY]
-
A.
Filey
chosen
Filey is a small seaside town and former fishing village on the North Sea coast of North Yorkshire, England, known for its long sandy beach and traditional holiday resort character.
-
B.
Filene's
Filene's was a historic American department store chain based in Boston, known for its flagship store and influential bargain basement retailing concept.
-
C.
Feyli
Feyli is a dialect of Southern Kurdish spoken primarily by the Feyli Kurds in parts of Iraq and Iran.
-
D.
Felletin
Felletin is a historic French town in the Creuse department renowned as a traditional center of tapestry weaving closely linked to the famed Aubusson tapestry industry.
-
E.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
- 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_69ca82f2dbe48190aba982e75a0d94de |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80728eb081909bae6aae45848fab |
completed | March 31, 2026, 8:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc775a548819090c83d916b352f41 |
completed | April 2, 2026, 1:33 a.m. |
Created at: March 30, 2026, 6 p.m.