Triple
T31981655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duke (Don Quixote) |
E816596
|
entity |
| Predicate | orchestratesPranksOn |
P61064
|
FINISHED |
| Object | Don Quixote |
—
|
NE NERFINISHED |
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: Don Quixote | Statement: [Duke (Don Quixote), orchestratesPranksOn, Don Quixote]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: orchestratesPranksOn Context triple: [Duke (Don Quixote), orchestratesPranksOn, Don Quixote]
-
A.
Prank Encounters
chosen
Indicates a relationship where one party orchestrates a deceptive or surprising prank scenario that another party unexpectedly experiences or becomes the target of.
-
B.
orchestratedFor
Indicates that one entity planned, coordinated, or arranged something specifically on behalf of or for the benefit of another entity.
-
C.
notablePrankTarget
Indicates that the subject is a well-known or frequent target of pranks carried out by the object.
-
D.
helpsOrchestrate
Indicates involvement in coordinating, organizing, or managing the execution of an activity or process, often in collaboration with others.
-
E.
orchestrationFor
Indicates a relationship where one entity coordinates, arranges, or manages the actions or interactions of another entity or set of entities to achieve a desired outcome.
- F. None of above.
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_69f348f6a3008190bfb59ca695fd68e2 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b34d116c81908086e458fe8bf42e |
completed | May 3, 2026, 2:30 a.m. |
| PD | Predicate disambiguation | batch_69f6b151ad008190836c1bcdec503ce2 |
completed | May 3, 2026, 2:22 a.m. |
Created at: May 1, 2026, 12:12 a.m.