Triple
T7436255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quest for Fire |
E171622
|
entity |
| Predicate | makeupArtist |
P53848
|
FINISHED |
| Object |
Michèle Burke
Michèle Burke is an acclaimed Irish-born makeup artist best known for her Oscar-winning work in film.
|
E688761
|
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: Michèle Burke | Statement: [Quest for Fire, makeupArtist, Michèle Burke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michèle Burke Context triple: [Quest for Fire, makeupArtist, Michèle Burke]
-
A.
Marie Burke
Marie Burke was a British actress and singer active in the early to mid-20th century, known for her work on stage, film, and radio.
-
B.
Lynne Burgess
Lynne Burgess was the wife of English novelist and composer Anthony Burgess, known primarily in relation to his life and work.
-
C.
Catherine McCarthy
Catherine McCarthy is an author and performance consultant best known for coauthoring the workplace and productivity book "The Way We’re Working Isn’t Working."
-
D.
Lorraine Broughton
Lorraine Broughton is a highly skilled, stylish MI6 spy and lethal combatant who serves as the protagonist of the action thriller film "Atomic Blonde."
-
E.
Maureen Earl
Maureen Earl is best known as the wife of American novelist Clifford Irving, who gained notoriety for his fraudulent "autobiography" of Howard Hughes.
- 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: Michèle Burke Triple: [Quest for Fire, makeupArtist, Michèle Burke]
Generated description
Michèle Burke is an acclaimed Irish-born makeup artist best known for her Oscar-winning work in film.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michèle Burke Target entity description: Michèle Burke is an acclaimed Irish-born makeup artist best known for her Oscar-winning work in film.
-
A.
Marie Burke
Marie Burke was a British actress and singer active in the early to mid-20th century, known for her work on stage, film, and radio.
-
B.
Lynne Burgess
Lynne Burgess was the wife of English novelist and composer Anthony Burgess, known primarily in relation to his life and work.
-
C.
Catherine McCarthy
Catherine McCarthy is an author and performance consultant best known for coauthoring the workplace and productivity book "The Way We’re Working Isn’t Working."
-
D.
Lorraine Broughton
Lorraine Broughton is a highly skilled, stylish MI6 spy and lethal combatant who serves as the protagonist of the action thriller film "Atomic Blonde."
-
E.
Maureen Earl
Maureen Earl is best known as the wife of American novelist Clifford Irving, who gained notoriety for his fraudulent "autobiography" of Howard Hughes.
- 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_69c68a64228c8190affaec2a8127ce7b |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f347f25081908e6086d4073295f5 |
completed | March 27, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8e56e62d48190b0e25464ebffe148 |
completed | March 29, 2026, 8:40 a.m. |
| NEDg | Description generation | batch_69c8e5f8f0408190abe815ce8f5f765f |
completed | March 29, 2026, 8:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8e644d67c819098c9d863cde99518 |
completed | March 29, 2026, 8:43 a.m. |
Created at: March 27, 2026, 3:13 p.m.