Triple
T22034630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gene Siskel |
E544169
|
entity |
| Predicate | partnerInWork |
P398
|
FINISHED |
| Object | Roger Ebert |
—
|
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: Roger Ebert | Statement: [Gene Siskel, partnerInWork, Roger Ebert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roger Ebert Context triple: [Gene Siskel, partnerInWork, Roger Ebert]
-
A.
Roger Ebert
chosen
Roger Ebert was a pioneering American film critic, journalist, and screenwriter renowned for his influential reviews, television programs, and role in popularizing accessible, mainstream film criticism.
-
B.
Gene Siskel
Gene Siskel was a prominent American film critic best known for his influential movie review television programs and long-running partnership with fellow critic Roger Ebert.
-
C.
Charlie Siskel
Charlie Siskel is an American film and television producer and director known for his work on documentaries and comedy programs.
-
D.
Ebert
Ebert is a German surname most notably associated with prominent political figures such as Friedrich Ebert, the first President of Germany, and his son Friedrich Ebert Jr.
-
E.
Kevin Turen
Kevin Turen is an American film and television producer known for working on acclaimed independent projects and prestige series.
- 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_69e11e2f98c8819083e11eab90942a78 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127f0594881909caf4fbc3e0a2d50 |
completed | April 28, 2026, 9:34 p.m. |
Created at: April 16, 2026, 8:24 p.m.