Triple
T12693547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dover Street |
E303270
|
entity |
| Predicate | renamedAs |
P65
|
FINISHED |
| Object | Green Park |
E57133
|
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: Green Park | Statement: [Dover Street, renamedAs, Green Park]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Green Park Context triple: [Dover Street, renamedAs, Green Park]
-
A.
Green Park
chosen
Green Park is a central London royal park known for its open lawns, mature trees, and tranquil atmosphere between Buckingham Palace and Piccadilly.
-
B.
Green Park
Green Park is a public park in Kanpur, India, historically significant enough to lend its name to the nearby Green Park Stadium.
-
C.
Green Park
Green Park is a Delhi Metro station in South Delhi serving the Green Park and nearby Hauz Khas and Safdarjung areas.
-
D.
Wickham Park
Wickham Park is a large public park in the Manchester area of Connecticut known for its landscaped gardens, walking trails, and scenic views.
-
E.
Grosvenor Park
Grosvenor Park is a large Victorian-era public park in Chester, England, known for its formal gardens, riverside setting, and historic features.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961dbc91c8190bec50797bbd593db |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d5ea02e08190b3be1fdfe86b4ee5 |
completed | May 3, 2026, 4:58 a.m. |
Created at: April 9, 2026, 5:22 p.m.