Triple
T13304184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dog Day Afternoon |
E316890
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Martin Elfand |
E316890
|
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: Martin Elfand | Statement: [Dog Day Afternoon, producer, Martin Elfand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martin Elfand Context triple: [Dog Day Afternoon, producer, Martin Elfand]
-
A.
Martin Elfand
chosen
Martin Elfand is an American film producer best known for his work on the acclaimed 1975 crime drama "Dog Day Afternoon."
-
B.
Uriel Weinreich
Uriel Weinreich was a prominent linguist known for his pioneering work in Yiddish studies, language contact, and sociolinguistics.
-
C.
Leon Feldhendler
Leon Feldhendler was a Polish Jewish resistance leader and Holocaust survivor best known for co-organizing the 1943 prisoner uprising at the Sobibor extermination camp.
-
D.
Max Zaslofsky
Max Zaslofsky was an American professional basketball player and coach, best known as one of the NBA’s early scoring stars and later a coach in the league.
-
E.
Azriel Rosenfeld
Azriel Rosenfeld was a pioneering computer scientist widely regarded as one of the founders of computer image analysis and digital image processing.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990a76adc8190ab9abcdb79a21ca8 |
completed | April 11, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f7a13508190bbab6eb68fb52a18 |
completed | May 3, 2026, 5:01 p.m. |
Created at: April 9, 2026, 9:28 p.m.