Triple
T13439035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweeney |
E320304
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Doug Sweeney
Doug Sweeney is a scholar of American religious history known for his work on Jonathan Edwards and evangelicalism.
|
E1102029
|
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: Doug Sweeney | Statement: [Sweeney, hasNotableBearer, Doug Sweeney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doug Sweeney Context triple: [Sweeney, hasNotableBearer, Doug Sweeney]
-
A.
Brian Souter
Brian Souter is a Scottish businessman best known as the co-founder of the Stagecoach Group and a prominent figure in the UK transport industry.
-
B.
Doug Bowne
Doug Bowne is a musician best known for his work with the new wave band Tom Tom Club.
-
C.
Kevin O'Connell
Kevin O'Connell is an American football coach and former NFL quarterback who serves as the head coach of the Minnesota Vikings.
-
D.
Doug Coughlin
Doug Coughlin is a fictional, hard-drinking mentor bartender from the 1988 film "Cocktail," known for his cynical life maxims and influence on the protagonist.
-
E.
Danny Coughlin
Danny Coughlin is the Irish-American Boston police officer protagonist of Dennis Lehane’s historical novel "The Given Day," set around the 1919 Boston Police Strike.
- 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: Doug Sweeney Triple: [Sweeney, hasNotableBearer, Doug Sweeney]
Generated description
Doug Sweeney is a scholar of American religious history known for his work on Jonathan Edwards and evangelicalism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Doug Sweeney Target entity description: Doug Sweeney is a scholar of American religious history known for his work on Jonathan Edwards and evangelicalism.
-
A.
Brian Souter
Brian Souter is a Scottish businessman best known as the co-founder of the Stagecoach Group and a prominent figure in the UK transport industry.
-
B.
Doug Bowne
Doug Bowne is a musician best known for his work with the new wave band Tom Tom Club.
-
C.
Kevin O'Connell
Kevin O'Connell is an American football coach and former NFL quarterback who serves as the head coach of the Minnesota Vikings.
-
D.
Doug Coughlin
Doug Coughlin is a fictional, hard-drinking mentor bartender from the 1988 film "Cocktail," known for his cynical life maxims and influence on the protagonist.
-
E.
Danny Coughlin
Danny Coughlin is the Irish-American Boston police officer protagonist of Dennis Lehane’s historical novel "The Given Day," set around the 1919 Boston Police Strike.
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaee5ec488190bd0c1e990dbd2bc2 |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6d6fde508190865a8e3e391fdf5e |
completed | May 8, 2026, 4:58 a.m. |
| NEDg | Description generation | batch_69fd6f53e9e08190b471e1698390f0c8 |
completed | May 8, 2026, 5:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd6fd126a881908baef34c1c013fe1 |
completed | May 8, 2026, 5:08 a.m. |
Created at: April 9, 2026, 9:40 p.m.