Triple
T6289889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ixelles |
E140988
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object | Forest |
E393674
|
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: Forest | Statement: [Ixelles, hasNeighbour, Forest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Forest Context triple: [Ixelles, hasNeighbour, Forest]
-
A.
Forest
Forest is the central protagonist of the game *Devs*, around whom the story’s main events and character dynamics revolve.
-
B.
Forest
chosen
Forest is a municipality in the Brussels-Capital Region of Belgium, known for its mix of residential areas, green spaces, and cultural venues.
-
C.
Woodland
Woodland is a small city in California’s Sacramento Valley known as an agricultural and administrative hub for Yolo County.
-
D.
Woodland
Woodland is a small, affluent residential city located in Hennepin County, Minnesota, known for its wooded landscapes and lakeside properties.
-
E.
Woodland
Woodland is an episode of the British nature documentary series "Wild Isles" that explores the wildlife and ecosystems of the United Kingdom’s forests and woodlands.
- 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_69c008cdf2ac8190bb640c94478fb4ed |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0641a6ecc8190a63be0e3f0344a3f |
completed | March 22, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c519794a9481909a72a8dcdfaf41f8 |
completed | March 26, 2026, 11:33 a.m. |
Created at: March 22, 2026, 4:26 p.m.