Triple
T16810608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suburban Commando |
E408597
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Ben Barenholtz |
E1196221
|
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: Ben Barenholtz | Statement: [Suburban Commando, producer, Ben Barenholtz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ben Barenholtz Context triple: [Suburban Commando, producer, Ben Barenholtz]
-
A.
Ben Barenholtz
chosen
Ben Barenholtz was an influential American film producer and distributor known for championing independent and art-house cinema, including early works by the Coen brothers.
-
B.
Avi Goldstein
Avi Goldstein is an individual notable enough to be recognized as a prominent bearer of the surname Goldstein.
-
C.
Tom Erez
Tom Erez is a researcher in machine learning and control, known for his work on deep reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG).
-
D.
Bryan Greenberg
Bryan Greenberg is an American actor and singer best known for his roles in television series like "One Tree Hill" and "How to Make It in America," as well as various romantic comedies.
-
E.
Uriel Feige
Uriel Feige is an Israeli computer scientist known for his influential work in computational complexity theory, approximation algorithms, and probabilistically checkable proofs.
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2cf680c8190bcd640570c524918 |
completed | April 18, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c79a96408190ab01ccd144f3fade |
completed | May 10, 2026, 5:59 p.m. |
Created at: April 10, 2026, 5:23 a.m.