Triple
T13996695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Name Is Michael Holbrook |
E336715
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Mark Crew
Mark Crew is a British record producer and songwriter known for his work with artists such as Bastille and MIKA.
|
E1075192
|
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: Mark Crew | Statement: [My Name Is Michael Holbrook, producer, Mark Crew]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Crew Context triple: [My Name Is Michael Holbrook, producer, Mark Crew]
-
A.
Ken Burnett
Ken Burnett is a prominent fundraising expert and author known for his influential work on donor relationship fundraising and nonprofit communications.
-
B.
Mark Sanger
Mark Sanger is a British film editor best known for his Academy Award–winning work on the science fiction thriller "Gravity."
-
C.
Michael Buckland
Michael Buckland is an American information scientist and librarian known for his influential work on information retrieval, library services, and the theory of information systems.
-
D.
Eric Crozier
Eric Crozier was a British theatrical director, producer, and writer best known for his close collaboration with composer Benjamin Britten on several operas.
-
E.
Mark Stevens
Mark Stevens was an American film and television actor best known for his roles in 1940s and 1950s dramas and film noir.
- 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: Mark Crew Triple: [My Name Is Michael Holbrook, producer, Mark Crew]
Generated description
Mark Crew is a British record producer and songwriter known for his work with artists such as Bastille and MIKA.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Crew Target entity description: Mark Crew is a British record producer and songwriter known for his work with artists such as Bastille and MIKA.
-
A.
Ken Burnett
Ken Burnett is a prominent fundraising expert and author known for his influential work on donor relationship fundraising and nonprofit communications.
-
B.
Mark Sanger
Mark Sanger is a British film editor best known for his Academy Award–winning work on the science fiction thriller "Gravity."
-
C.
Michael Buckland
Michael Buckland is an American information scientist and librarian known for his influential work on information retrieval, library services, and the theory of information systems.
-
D.
Eric Crozier
Eric Crozier was a British theatrical director, producer, and writer best known for his close collaboration with composer Benjamin Britten on several operas.
-
E.
Mark Stevens
Mark Stevens was an American film and television actor best known for his roles in 1940s and 1950s dramas and film noir.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2eb68ba88190bfaf10777d607bf3 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc32789e08190be0f1d1685dcc90e |
completed | May 6, 2026, 10:39 p.m. |
| NEDg | Description generation | batch_69fbc6d1048081908fb2e798cbc9902f |
completed | May 6, 2026, 10:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fbc76610008190bd3c7f357666c8db |
completed | May 6, 2026, 10:57 p.m. |
Created at: April 9, 2026, 10:19 p.m.