Triple
T22956956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Shots |
E570785
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object |
Michael Katleman
Michael Katleman is an American television director and producer known for his work on various drama series and network shows.
|
E1563609
|
NE FINISHED |
How this triple was built (4 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: Michael Katleman | Statement: [Big Shots, executiveProducer, Michael Katleman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Katleman Context triple: [Big Shots, executiveProducer, Michael Katleman]
-
A.
Michael Neeleman
Michael Neeleman is a notable individual recognized as a bearer of the Neeleman surname, likely distinguished in a professional or public context.
-
B.
Michael Hecht
Michael Hecht is the birth name of Michael Howard, a British Conservative politician who served as Leader of the Opposition and Home Secretary.
-
C.
Michael Hecht
Michael Hecht is a scientist best known for leading NASA’s MOXIE experiment on the Perseverance rover, which demonstrates in-situ oxygen production on Mars.
-
D.
Michael Jaffe
Michael Jaffe is an American television and film producer known for his work on numerous TV movies, series, and feature films.
-
E.
Michael Tuchner
Michael Tuchner was a British film and television director known for his work on crime dramas and character-driven stories in the 1960s and 1970s.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Michael Katleman Triple: [Big Shots, executiveProducer, Michael Katleman]
Generated description
Michael Katleman is an American television director and producer known for his work on various drama series and network shows.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Katleman Target entity description: Michael Katleman is an American television director and producer known for his work on various drama series and network shows.
-
A.
Michael Neeleman
Michael Neeleman is a notable individual recognized as a bearer of the Neeleman surname, likely distinguished in a professional or public context.
-
B.
Michael Hecht
Michael Hecht is the birth name of Michael Howard, a British Conservative politician who served as Leader of the Opposition and Home Secretary.
-
C.
Michael Hecht
Michael Hecht is a scientist best known for leading NASA’s MOXIE experiment on the Perseverance rover, which demonstrates in-situ oxygen production on Mars.
-
D.
Michael Jaffe
Michael Jaffe is an American television and film producer known for his work on numerous TV movies, series, and feature films.
-
E.
Michael Tuchner
Michael Tuchner was a British film and television director known for his work on crime dramas and character-driven stories in the 1960s and 1970s.
- F. None of above. chosen
Provenance (5 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_69e245b212a88190b5259caf51606084 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f181f15ebc8190bf477539f7c23985 |
completed | April 29, 2026, 3:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bca1bbba0819097115ef1d032d3f6 |
completed | May 19, 2026, 2:25 a.m. |
| NEDg | Description generation | batch_6a0bcaebed688190b2ea74c588922d98 |
completed | May 19, 2026, 2:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bcbec354081909dbeeb389e86d38c |
completed | May 19, 2026, 2:33 a.m. |
Created at: April 17, 2026, 3:47 p.m.