Triple
T9452430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Analog Devices |
E227925
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Matthew Lorber
Matthew Lorber is an engineer and entrepreneur best known as a co-founder of the semiconductor company Analog Devices.
|
E847060
|
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: Matthew Lorber | Statement: [Analog Devices, foundedBy, Matthew Lorber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Lorber Context triple: [Analog Devices, foundedBy, Matthew Lorber]
-
A.
Michael Leibert
Michael Leibert was an American theater director and producer best known for establishing the influential Berkeley Repertory Theatre in California.
-
B.
Steven Baigelman
Steven Baigelman is an American screenwriter and producer known for his work on biographical and crime dramas in film and television.
-
C.
Eric Ladin
Eric Ladin is an American actor known for his roles in television series such as "Generation Kill," "The Killing," and "Boardwalk Empire."
-
D.
Michael Kozoll
Michael Kozoll is an American television writer and producer best known for co-creating the influential police drama series "Hill Street Blues."
-
E.
Michael Haussman
Michael Haussman is an American director and filmmaker best known for his work on high-profile music videos and commercials.
- 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: Matthew Lorber Triple: [Analog Devices, foundedBy, Matthew Lorber]
Generated description
Matthew Lorber is an engineer and entrepreneur best known as a co-founder of the semiconductor company Analog Devices.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matthew Lorber Target entity description: Matthew Lorber is an engineer and entrepreneur best known as a co-founder of the semiconductor company Analog Devices.
-
A.
Michael Leibert
Michael Leibert was an American theater director and producer best known for establishing the influential Berkeley Repertory Theatre in California.
-
B.
Steven Baigelman
Steven Baigelman is an American screenwriter and producer known for his work on biographical and crime dramas in film and television.
-
C.
Eric Ladin
Eric Ladin is an American actor known for his roles in television series such as "Generation Kill," "The Killing," and "Boardwalk Empire."
-
D.
Michael Kozoll
Michael Kozoll is an American television writer and producer best known for co-creating the influential police drama series "Hill Street Blues."
-
E.
Michael Haussman
Michael Haussman is an American director and filmmaker best known for his work on high-profile music videos and commercials.
- 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_69ca8439f8bc8190997f2ef40c9f0bc2 |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f68f9b081908bee041d4fc77e57 |
completed | April 1, 2026, 8:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d316faaed48190aa51e52b5b774cd8 |
completed | April 6, 2026, 2:14 a.m. |
| NEDg | Description generation | batch_69d3183a8410819094e81fe9f43717b2 |
completed | April 6, 2026, 2:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d318adfcb081909a3567f5327765ab |
completed | April 6, 2026, 2:21 a.m. |
Created at: March 30, 2026, 7:52 p.m.