Triple
T3046348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Republic Polytechnic campus |
E83456
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Woodlands |
E177455
|
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: [Republic Polytechnic campus, region, Woodlands]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Woodlands Context triple: [Republic Polytechnic campus, region, Woodlands]
-
A.
Woodlands
Woodlands is a residential neighbourhood located within the city of Pickering in Ontario, Canada.
-
B.
Woodlands
Woodlands is a natural, forested area within Belle Isle Park that offers visitors scenic trails and a tranquil escape into nature.
-
C.
Woodlands
chosen
Woodlands is a residential and commercial town in northern Singapore that serves as a key land border crossing point to Malaysia across the Straits of Johor.
-
D.
Woodland
Woodland is a small city in California’s Sacramento Valley known as an agricultural and administrative hub for Yolo County.
-
E.
Islington Woods
Islington Woods is a residential neighbourhood in the city of Vaughan, Ontario, known for its green spaces and suburban character.
- 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_69ad8b24924c8190a9bb6f61d519e4ae |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9bab541c8190a17aca26b3dcfae7 |
completed | March 8, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1eef43da8819094cd438c93c64c86 |
completed | March 11, 2026, 10:38 p.m. |
Created at: March 8, 2026, 3:01 p.m.