Triple
T910033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yolo County |
E19636
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Woodland |
E107447
|
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: Woodland | Statement: [Yolo County, hasCity, Woodland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Woodland Context triple: [Yolo County, hasCity, Woodland]
-
A.
Woodland
chosen
Woodland is a small city in California’s Sacramento Valley known as an agricultural and administrative hub for Yolo County.
-
B.
North Woods
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.
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.
-
D.
Rumsey Woods
Rumsey Woods is a wooded area within Buffalo’s historic park and parkway system, known for its natural landscape and recreational green space.
-
E.
Argonne Forest
Argonne Forest is a wooded region in northeastern France that was a major World War I battlefield, particularly known for the Meuse-Argonne Offensive.
- 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_69a4939f91a08190ba68c2c81eab90fe |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b2dca5208190bc9f17cd9dd6a98f |
completed | March 1, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7cf5c4acc8190a0f72ca30f187ed1 |
completed | March 4, 2026, 6:21 a.m. |
Created at: March 1, 2026, 7:39 p.m.