Triple
T22938873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Percy Pickering |
E569663
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Percy |
—
|
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: Percy | Statement: [Percy Pickering, hasGivenName, Percy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Percy Context triple: [Percy Pickering, hasGivenName, Percy]
-
A.
Percy
Percy is a character connected to the Paradise Falls diner, likely serving as one of its notable staff or regular patrons within its narrative setting.
-
B.
Percy
Percy is a small green saddle tank engine from the Thomas & Friends franchise, known for his youthful enthusiasm, loyalty, and occasional nervousness.
-
C.
Percy
chosen
Percy is a masculine given name of Old French origin, famously borne by American physicist and Nobel laureate Percy W. Bridgman.
-
D.
Percy
Percy is the historic English noble family that produced numerous prominent aristocrats, soldiers, and politicians, notably the Dukes of Northumberland.
-
E.
Percy
Percy is a sadistic and cowardly prison guard from Stephen King’s novel "The Green Mile" and its film adaptation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e24590862c8190858f180ad302adab |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f181378660819083239986c846ec17 |
completed | April 29, 2026, 3:55 a.m. |
Created at: April 17, 2026, 3:45 p.m.