Triple
T5077545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julian Barnes |
E114434
|
entity |
| Predicate | hasPseudonym |
P3799
|
FINISHED |
| Object |
Dan Kavanagh
Dan Kavanagh is the crime-fiction pseudonym used by British novelist Julian Barnes for a series of detective novels.
|
E498884
|
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 Kavanagh | Statement: [Julian Barnes, hasPseudonym, Dan Kavanagh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Kavanagh Context triple: [Julian Barnes, hasPseudonym, Dan Kavanagh]
-
A.
Brian Callaghan
Brian Callaghan is a personal name shared by multiple individuals, typically of Irish or British origin, who may be notable in various professional or public contexts.
-
B.
Andrew Duggan
Andrew Duggan was an American character actor known for his prolific work in film and television from the 1950s through the 1980s.
-
C.
Kevin Flanagan
Kevin Flanagan is a personal name shared by multiple individuals, including professionals and public figures in various fields.
-
D.
Danny Noonan
Danny Noonan is the young, ambitious golf caddie who serves as the central protagonist in the comedy film "Caddyshack."
-
E.
Brendan Vaughan
Brendan Vaughan is an American media executive and journalist best known as the editor-in-chief of the business and innovation magazine Fast Company.
- 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 Kavanagh Triple: [Julian Barnes, hasPseudonym, Dan Kavanagh]
Generated description
Dan Kavanagh is the crime-fiction pseudonym used by British novelist Julian Barnes for a series of detective novels.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dan Kavanagh Target entity description: Dan Kavanagh is the crime-fiction pseudonym used by British novelist Julian Barnes for a series of detective novels.
-
A.
Brian Callaghan
Brian Callaghan is a personal name shared by multiple individuals, typically of Irish or British origin, who may be notable in various professional or public contexts.
-
B.
Andrew Duggan
Andrew Duggan was an American character actor known for his prolific work in film and television from the 1950s through the 1980s.
-
C.
Kevin Flanagan
Kevin Flanagan is a personal name shared by multiple individuals, including professionals and public figures in various fields.
-
D.
Danny Noonan
Danny Noonan is the young, ambitious golf caddie who serves as the central protagonist in the comedy film "Caddyshack."
-
E.
Brendan Vaughan
Brendan Vaughan is an American media executive and journalist best known as the editor-in-chief of the business and innovation magazine Fast Company.
- 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_69bd443dbf908190a9401e9c2dc7bd7d |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd74f632788190ac4fd047e1a20485 |
completed | March 20, 2026, 4:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed912629c8190beffde376f0aedc7 |
completed | March 21, 2026, 5:44 p.m. |
| NEDg | Description generation | batch_69bed97f929881909af270c910cdbff1 |
completed | March 21, 2026, 5:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bed9d7f47c819089a6f8d022291929 |
completed | March 21, 2026, 5:48 p.m. |
Created at: March 20, 2026, 1:39 p.m.