Triple
T19235499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sacha Pitoëff |
E480980
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Inferno |
—
|
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: Inferno | Statement: [Sacha Pitoëff, notableWork, Inferno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Inferno Context triple: [Sacha Pitoëff, notableWork, Inferno]
-
A.
Inferno
Inferno is the first cantica of Dante Alighieri’s Divine Comedy, depicting the poet’s allegorical journey through the nine circles of Hell.
-
B.
Inferno
chosen
"Inferno" is a 1953 Technicolor 3D film noir thriller starring William Lundigan alongside Robert Ryan and Rhonda Fleming, noted for its desert survival plot and innovative use of 3D cinematography.
-
C.
Inferno
"Inferno" is a 1980s action thriller film best known for its desert survival and revenge storyline, directed by John G. Avildsen.
-
D.
Inferno
Inferno is a major expansion for the sci-fi MMORPG EVE Online that focused on revamping warfare mechanics, including factional warfare and mercenary contracts.
-
E.
Inferno
"Inferno" is a 2016 mystery thriller film based on Dan Brown's novel, in which Irrfan Khan plays a key supporting role alongside Tom Hanks.
- 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_69d8e8ccb8f48190ad420098e74fb1db |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5faec6d0c8190b90cb1bb3160a847 |
completed | April 20, 2026, 10:07 a.m. |
Created at: April 10, 2026, 1:26 p.m.