Triple
T20377447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Notting Dale |
E497729
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Holland Park |
—
|
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: Holland Park | Statement: [Notting Dale, locatedNear, Holland Park]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Holland Park Context triple: [Notting Dale, locatedNear, Holland Park]
-
A.
Holland Park
chosen
Holland Park is a leafy, affluent district and public park in west London known for its elegant townhouses, landscaped gardens, and cultural attractions.
-
B.
Holborn Park
Holborn Park is a public green space in Berkeley, California, offering local residents recreational areas and outdoor amenities.
-
C.
Green Park
Green Park is a public park in Kanpur, India, historically significant enough to lend its name to the nearby Green Park Stadium.
-
D.
Green Park
Green Park is a central London royal park known for its open lawns, mature trees, and tranquil atmosphere between Buckingham Palace and Piccadilly.
-
E.
Green Park
Green Park is a Delhi Metro station in South Delhi serving the Green Park and nearby Hauz Khas and Safdarjung areas.
- 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_69e0b4a5b7908190a972e4e7e698ae94 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e678ae3468819087d7db7e1b79efc7 |
completed | April 20, 2026, 7:04 p.m. |
Created at: April 16, 2026, 11:27 a.m.