Triple
T3287436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Facinelli |
E69016
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Peter Facinelli |
E69016
|
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: Peter Facinelli | Statement: [Peter Facinelli, name, Peter Facinelli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Facinelli Context triple: [Peter Facinelli, name, Peter Facinelli]
-
A.
Peter Facinelli
chosen
Peter Facinelli is an American actor best known for playing Dr. Carlisle Cullen in the Twilight film series.
-
B.
Brett Cullen
Brett Cullen is an American actor known for his numerous film and television roles, including playing Thomas Wayne in the 2019 film "Joker."
-
C.
Michael Vidal
Michael Vidal is a local political leader who serves as the mayor of the Maltese town of Ramla.
-
D.
Joe Letteri
Joe Letteri is a renowned visual effects supervisor best known for his groundbreaking work on films like The Lord of the Rings series, Avatar, and The Hobbit trilogy.
-
E.
Paul Lo Duca
Paul Lo Duca is a former Major League Baseball catcher best known for his All-Star seasons with the Los Angeles Dodgers and New York Mets.
- 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_69ad859d45748190b0742408c954b39f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb058e00881908fdf0a23208860d4 |
completed | March 8, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e85f71508190b194b4d383d7ee32 |
completed | March 12, 2026, 4:22 p.m. |
Created at: March 8, 2026, 3:10 p.m.