Triple
T3398725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flanagan |
E71593
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
David Flanagan
David Flanagan is a software developer and technical author best known for his widely used programming books, including "JavaScript: The Definitive Guide."
|
E355100
|
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: David Flanagan | Statement: [Flanagan, hasNotableBearer, David Flanagan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Flanagan Context triple: [Flanagan, hasNotableBearer, David Flanagan]
-
A.
Matthew C. Brown
Matthew C. Brown is a film producer known for his work on the horror movie "Spectral."
-
B.
David Heitner
David Heitner is a film editor known for his work on the South African musical drama film "Sarafina!".
-
C.
Michael Feathers
Michael Feathers is a software engineer, consultant, and author known for his influential work on legacy code, refactoring, and improving software design and maintainability.
-
D.
Eric Evans
Eric Evans is a software engineer and thought leader best known for originating and popularizing the concept of Domain-Driven Design in enterprise software development.
-
E.
Dan Goldberg
Dan Goldberg is a film producer best known for his work on major Hollywood comedies, including the hit movie "The Hangover."
- 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: David Flanagan Triple: [Flanagan, hasNotableBearer, David Flanagan]
Generated description
David Flanagan is a software developer and technical author best known for his widely used programming books, including "JavaScript: The Definitive Guide."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: David Flanagan Target entity description: David Flanagan is a software developer and technical author best known for his widely used programming books, including "JavaScript: The Definitive Guide."
-
A.
Matthew C. Brown
Matthew C. Brown is a film producer known for his work on the horror movie "Spectral."
-
B.
David Heitner
David Heitner is a film editor known for his work on the South African musical drama film "Sarafina!".
-
C.
Michael Feathers
Michael Feathers is a software engineer, consultant, and author known for his influential work on legacy code, refactoring, and improving software design and maintainability.
-
D.
Eric Evans
Eric Evans is a software engineer and thought leader best known for originating and popularizing the concept of Domain-Driven Design in enterprise software development.
-
E.
Dan Goldberg
Dan Goldberg is a film producer best known for his work on major Hollywood comedies, including the hit movie "The Hangover."
- 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_69ad85aac4808190a092c9cc8911f584 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb8c5816881909f91e6e9b81d29e3 |
completed | March 8, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b34bd01e108190995dcd3cb8ead793 |
completed | March 12, 2026, 11:27 p.m. |
| NEDg | Description generation | batch_69b34e46b2b48190aedee8dabf5285bd |
completed | March 12, 2026, 11:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b34fc0b830819082b50ebd14b6490b |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 8, 2026, 3:14 p.m.