Triple
T256609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Park |
E5448
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Woodlands |
E33090
|
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: Woodlands | Statement: [Central Park, hasPart, Woodlands]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Woodlands Context triple: [Central Park, hasPart, Woodlands]
-
A.
South Woods
South Woods is a wooded area within New York City's Central Park known for its naturalistic landscape and tranquil, forest-like setting.
-
B.
North Woods
chosen
North Woods is a large, wooded section of New York City's Central Park designed to evoke a natural forest retreat within the urban landscape.
-
C.
Barnsdale Forest
Barnsdale Forest is a historic woodland area in South Yorkshire, England, traditionally associated with the legendary outlaw Robin Hood.
-
D.
de Forest
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
-
E.
Sarah Doublet Forest
Sarah Doublet Forest is a protected conservation area in Littleton, Massachusetts, known for its wooded trails, wildlife habitat, and opportunities for passive outdoor recreation.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25d5884c88190a349d7593b688921 |
completed | Feb. 28, 2026, 3:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a386170dac81909a5ebf631f6037ab |
completed | March 1, 2026, 12:19 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.