Triple
T22356543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Will Forte |
E552666
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Orville |
—
|
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: Orville | Statement: [Will Forte, givenName, Orville]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orville Context triple: [Will Forte, givenName, Orville]
-
A.
Orville
Orville is the comically clumsy albatross who serves as the bumbling but brave air transport for the heroes in Disney’s animated film "The Rescuers."
-
B.
Orville
chosen
Orville is a masculine given name of English origin that has been borne by various notable figures, including scientists, inventors, and pioneers.
-
C.
Orville Dewey
Orville Dewey was a 19th-century American Unitarian minister and theologian known for his liberal religious views and influential sermons.
-
D.
Arville
Arville is a small commune in the Loir-et-Cher department of central France, known for its rural character and historical ties to the Vendôme area.
-
E.
Orville Wingait
Orville Wingait is a comedic, hapless protagonist portrayed by Eddie Bracken in mid-20th-century American film.
- 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_69e11e4a0ad08190a385b4d343cf6524 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f157d08b148190a9a4e445e8579219 |
completed | April 29, 2026, 12:58 a.m. |
Created at: April 16, 2026, 8:44 p.m.