Triple
T4274689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glen Ellen |
E97020
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Kenwood |
E92008
|
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: Kenwood | Statement: [Glen Ellen, locatedNear, Kenwood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenwood Context triple: [Glen Ellen, locatedNear, Kenwood]
-
A.
Kenwood
chosen
Kenwood is a small community in California’s Sonoma Valley known for its wineries, vineyards, and scenic rural charm.
-
B.
Kenwood
Kenwood is a historic neighborhood within Dracut, Massachusetts, known for its preserved architecture and local heritage.
-
C.
Kenwood
Kenwood is a historic, affluent neighborhood on Chicago’s South Side known for its lakefront location, notable architecture, and prominent residents.
-
D.
Kenwood
Kenwood is an affluent residential neighborhood in Minneapolis, Minnesota, known for its historic homes and proximity to the city's scenic lakes.
-
E.
Sanyo
Sanyo is a Japanese electronics brand known for producing a wide range of consumer and industrial electronic products, including televisions, batteries, and home appliances.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501abb74819086b2f04ac7a5c114 |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7b0b2ec819090ccf042917ae207 |
completed | March 14, 2026, 7:32 p.m. |
Created at: March 12, 2026, 11:07 p.m.