Triple
T9329022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Holmes |
E224467
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Grant Bowler
Grant Bowler is an Australian actor known for his roles in television series such as "Defiance," "Ugly Betty," and "True Blood."
|
E796601
|
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: Grant Bowler | Statement: [Peter Holmes, portrayedBy, Grant Bowler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grant Bowler Context triple: [Peter Holmes, portrayedBy, Grant Bowler]
-
A.
James Bowler
James Bowler is a senior British civil servant who serves as the Permanent Secretary to HM Treasury, overseeing the UK government's economic and financial administration.
-
B.
Stephen Bowen
Stephen Bowen is a former American football defensive end who played in the NFL, most notably for the Dallas Cowboys and Washington Redskins.
-
C.
Sean Barton
Sean Barton is a film editor known for his work on various feature films, including the drama "Tea with Mussolini."
-
D.
Mike Boodley
Mike Boodley is a roller coaster designer best known for his work on major thrill rides, including the wooden coaster Roar.
-
E.
Grant Bardsley
Grant Bardsley is a British voice actor best known for voicing the protagonist Taran in Disney’s animated film "The Black Cauldron."
- 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: Grant Bowler Triple: [Peter Holmes, portrayedBy, Grant Bowler]
Generated description
Grant Bowler is an Australian actor known for his roles in television series such as "Defiance," "Ugly Betty," and "True Blood."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Grant Bowler Target entity description: Grant Bowler is an Australian actor known for his roles in television series such as "Defiance," "Ugly Betty," and "True Blood."
-
A.
James Bowler
James Bowler is a senior British civil servant who serves as the Permanent Secretary to HM Treasury, overseeing the UK government's economic and financial administration.
-
B.
Stephen Bowen
Stephen Bowen is a former American football defensive end who played in the NFL, most notably for the Dallas Cowboys and Washington Redskins.
-
C.
Sean Barton
Sean Barton is a film editor known for his work on various feature films, including the drama "Tea with Mussolini."
-
D.
Mike Boodley
Mike Boodley is a roller coaster designer best known for his work on major thrill rides, including the wooden coaster Roar.
-
E.
Grant Bardsley
Grant Bardsley is a British voice actor best known for voicing the protagonist Taran in Disney’s animated film "The Black Cauldron."
- 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_69ca8427a0c08190b749831d5ea98f02 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd37ab90ac8190b5c73f08dd091731 |
completed | April 1, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d100c48be481908b670d1e30a9c7e2 |
completed | April 4, 2026, 12:15 p.m. |
| NEDg | Description generation | batch_69d1023423e8819096a540f7437bf484 |
completed | April 4, 2026, 12:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1028799a8819086d73c93d47ab33b |
completed | April 4, 2026, 12:22 p.m. |
Created at: March 30, 2026, 7:39 p.m.