Triple
T7549270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fagan |
E178487
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Dan Fagan
Dan Fagan is a notable individual recognized for achievements associated with the surname Fagan.
|
E688878
|
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: Dan Fagan | Statement: [Fagan, hasNotableBearer, Dan Fagan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Fagan Context triple: [Fagan, hasNotableBearer, Dan Fagan]
-
A.
Matt Fenton
Matt Fenton is a British theatre director and arts leader known for his innovative, youth-focused programming and leadership within the UK performing arts sector.
-
B.
Dan Kavanagh
Dan Kavanagh is the crime-fiction pseudonym used by British novelist Julian Barnes for a series of detective novels.
-
C.
Greg Finton
Greg Finton is a film editor known for his work on documentaries and feature films, including the acclaimed documentary "He Named Me Malala."
-
D.
Brian Flanagan
Brian Flanagan is a fictional bartender and main character from the 1988 film "Cocktail," portrayed by Tom Cruise.
-
E.
Jeff Fahey
Jeff Fahey is an American actor known for his work in film and television, including roles in genre projects like the grindhouse-style horror film "Planet Terror."
- 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: Dan Fagan Triple: [Fagan, hasNotableBearer, Dan Fagan]
Generated description
Dan Fagan is a notable individual recognized for achievements associated with the surname Fagan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dan Fagan Target entity description: Dan Fagan is a notable individual recognized for achievements associated with the surname Fagan.
-
A.
Matt Fenton
Matt Fenton is a British theatre director and arts leader known for his innovative, youth-focused programming and leadership within the UK performing arts sector.
-
B.
Dan Kavanagh
Dan Kavanagh is the crime-fiction pseudonym used by British novelist Julian Barnes for a series of detective novels.
-
C.
Greg Finton
Greg Finton is a film editor known for his work on documentaries and feature films, including the acclaimed documentary "He Named Me Malala."
-
D.
Brian Flanagan
Brian Flanagan is a fictional bartender and main character from the 1988 film "Cocktail," portrayed by Tom Cruise.
-
E.
Jeff Fahey
Jeff Fahey is an American actor known for his work in film and television, including roles in genre projects like the grindhouse-style horror film "Planet Terror."
- 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_69c69f2cbe08819088f9eb0c03ef529b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8b35ba481908e1e5bbf329daa33 |
completed | March 27, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8e575c22481908a6779f5d496bd3a |
completed | March 29, 2026, 8:40 a.m. |
| NEDg | Description generation | batch_69c8e667a4c48190aee42aa003c4202b |
completed | March 29, 2026, 8:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8e6f7ae88819085e600a1266580d3 |
completed | March 29, 2026, 8:46 a.m. |
Created at: March 27, 2026, 3:49 p.m.