Triple
T2789258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pieter Jan Brugge |
E61888
|
entity |
| Predicate | workedOn |
P3
|
FINISHED |
| Object | The Clearing |
E255992
|
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: The Clearing | Statement: [Pieter Jan Brugge, workedOn, The Clearing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Clearing Context triple: [Pieter Jan Brugge, workedOn, The Clearing]
-
A.
The Clearing
chosen
The Clearing is a psychological thriller film featuring Miranda Otto in a prominent role.
-
B.
The Woods
The Woods is a residential neighborhood within the planned community of Burke Centre in Fairfax County, Virginia.
-
C.
The Meadow
The Meadow is a large open green space within Delaware Park in Buffalo, New York, commonly used for recreation, events, and outdoor gatherings.
-
D.
The Ravine
The Ravine is a secluded, woodland gorge in Central Park featuring winding paths, a stream, and rustic bridges that evoke a natural forest landscape.
-
E.
Wildwood
Wildwood is a popular seaside resort city on the Jersey Shore known for its expansive beaches, lively boardwalk, and classic Doo Wop–style motels.
- 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_69ab4b7f51d881908768300ebd2fbdae |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddb3d63c8190b3ab5fa363c69db8 |
completed | March 7, 2026, 8:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce8e2c488190a63332f36803c295 |
completed | March 10, 2026, 7:55 a.m. |
Created at: March 6, 2026, 9:58 p.m.