Triple
T5948709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Brody |
E132342
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Mark Gruner |
E220954
|
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: Mark Gruner | Statement: [Michael Brody, portrayedBy, Mark Gruner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Gruner Context triple: [Michael Brody, portrayedBy, Mark Gruner]
-
A.
Mark Gruner
chosen
Mark Gruner is an American former child actor best known for his role in the 1978 thriller film "Jaws 2."
-
B.
Greg Grunberg
Greg Grunberg is an American actor best known for his roles in television series such as "Heroes," "Alias," and "Felicity," as well as appearances in major film franchises.
-
C.
Michael Gruskoff
Michael Gruskoff is an American film producer best known for his work on influential 1970s and 1980s films, including the cult science fiction movie "Silent Running."
-
D.
Michael Blum
Michael Blum is best known as the husband of comedian and actress Julia Sweeney.
-
E.
Eric Brenner
Eric Brenner is a film producer known for his work on independent movies, including the action drama "Mercury Plains."
- 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_69c00869d3308190af89b2453e0f7546 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0397deea08190b9397d0413740300 |
completed | March 22, 2026, 6:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c08d4f0481908547609bc2736380 |
completed | March 23, 2026, 4:24 a.m. |
Created at: March 22, 2026, 4:01 p.m.