Triple
T2783015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eastside of King County |
E61740
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Bellevue |
E24777
|
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: Bellevue | Statement: [Eastside of King County, hasPart, Bellevue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bellevue Context triple: [Eastside of King County, hasPart, Bellevue]
-
A.
Bellevue
Bellevue is a small municipality located along Lake Geneva in the canton of Geneva in southwestern Switzerland.
-
B.
Kirkland
Kirkland is a suburban town located on the Island of Montreal in the Canadian province of Quebec.
-
C.
Tukwila
Tukwila is a suburban city just south of Seattle, Washington, known as a regional transportation and retail hub.
-
D.
City of Bellevue
The City of Bellevue is a major suburban municipality in Washington State, known for its thriving downtown, tech industry presence, and role as an economic and cultural hub of the Seattle metropolitan area.
-
E.
Bellevue, Washington, United States
chosen
Bellevue, Washington, United States is a major city in the Seattle metropolitan area known for its thriving tech industry, upscale downtown, and role as a regional business and retail hub.
- 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_69ab4b7e43c48190997b8fc8fb1663ab |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd9cd950819083770d9f0516a9cf |
completed | March 7, 2026, 8:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b57f0432e081908110ede1c2b7a54a |
completed | March 14, 2026, 3:30 p.m. |
Created at: March 6, 2026, 9:57 p.m.