Triple
T7577789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bourke |
E179401
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Michael Bourke
Michael Bourke is a personal name shared by multiple individuals, including various professionals and public figures across different fields.
|
E675004
|
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: Michael Bourke | Statement: [Bourke, hasNotableBearer, Michael Bourke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Bourke Context triple: [Bourke, hasNotableBearer, Michael Bourke]
-
A.
Michael Byrne
Michael Byrne is a British character actor known for his numerous film and television roles, often portraying military officers or authority figures.
-
B.
Michael Barrett
Michael Barrett is an American cinematographer known for his work on numerous feature films and television projects, including mainstream comedies and action movies.
-
C.
Ian Mackley
Ian Mackley is the husband of British comedian and television personality Julian Clary.
-
D.
Andrew Duggan
Andrew Duggan was an American character actor known for his prolific work in film and television from the 1950s through the 1980s.
-
E.
Matthew Rolph
Matthew Rolph is an American actor and comedian best known for his marriage to actress and comedian Mary Lynn Rajskub.
- 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: Michael Bourke Triple: [Bourke, hasNotableBearer, Michael Bourke]
Generated description
Michael Bourke is a personal name shared by multiple individuals, including various professionals and public figures across different fields.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Bourke Target entity description: Michael Bourke is a personal name shared by multiple individuals, including various professionals and public figures across different fields.
-
A.
Michael Byrne
Michael Byrne is a British character actor known for his numerous film and television roles, often portraying military officers or authority figures.
-
B.
Michael Barrett
Michael Barrett is an American cinematographer known for his work on numerous feature films and television projects, including mainstream comedies and action movies.
-
C.
Ian Mackley
Ian Mackley is the husband of British comedian and television personality Julian Clary.
-
D.
Andrew Duggan
Andrew Duggan was an American character actor known for his prolific work in film and television from the 1950s through the 1980s.
-
E.
Matthew Rolph
Matthew Rolph is an American actor and comedian best known for his marriage to actress and comedian Mary Lynn Rajskub.
- 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_69c69f327db881909a21ae3b156f8ded |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f94cdbec81909f2ba7ce04e49931 |
completed | March 27, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8616d87f881909fe23220dc77167c |
completed | March 28, 2026, 11:17 p.m. |
| NEDg | Description generation | batch_69c8625282bc8190bd1eecc13c1d4744 |
completed | March 28, 2026, 11:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8630fe8608190afc7b67d9a80240b |
completed | March 28, 2026, 11:24 p.m. |
Created at: March 27, 2026, 3:51 p.m.