Triple
T20618333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whistler Tower |
E506625
|
entity |
| Predicate | isInDistrict |
P16988
|
FINISHED |
| Object | Chelsea |
—
|
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: Chelsea | Statement: [Whistler Tower, isInDistrict, Chelsea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chelsea Context triple: [Whistler Tower, isInDistrict, Chelsea]
-
A.
Chelsea
Chelsea is a small municipality in western Quebec, Canada, known for its scenic natural surroundings and proximity to Gatineau Park.
-
B.
Chelsea
Chelsea is a vampire in the Twilight series’ Volturi coven, known for her power to manipulate emotional bonds and loyalties.
-
C.
Chelsea
chosen
Chelsea is an affluent district in West London known for its upscale residential streets, fashionable boutiques, and cultural landmarks along the River Thames.
-
D.
Chelsea
"Chelsea" is a historical work by British writer Thea Holme that portrays the social and cultural life of the Chelsea district in London.
-
E.
Chelsea
Chelsea is a small city in southeastern Michigan, known for its historic downtown, arts scene, and proximity to Ann Arbor.
- 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_69e0b4bc90988190ac360aaf645efc1d |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6abdf9d7c8190969247a4ae55b781 |
completed | April 20, 2026, 10:42 p.m. |
Created at: April 16, 2026, 11:41 a.m.