Triple
T18264822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ungar |
E437455
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Michael Ungar
Michael Ungar is a Canadian researcher and author best known for his work on resilience in children, families, and communities.
|
E1315900
|
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 Ungar | Statement: [Ungar, hasNotableBearer, Michael Ungar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Ungar Context triple: [Ungar, hasNotableBearer, Michael Ungar]
-
A.
Daniel Shere
Daniel Shere is a screenwriter best known for his work on the animated film "Epic."
-
B.
Richard Leacock
Richard Leacock was a pioneering British-born documentary filmmaker and key figure in the development of cinéma vérité and direct cinema.
-
C.
John Neufeld
John Neufeld is a film music orchestrator known for his work on major Hollywood scores, including Disney’s animated feature "Mulan" (1998).
-
D.
Michael Krupat
Michael Krupat is a television producer best known for creating the popular Food Network cooking competition series "Chopped."
-
E.
Bonnie Piesse
Bonnie Piesse is an Australian actress and singer best known for playing Beru Lars in the Star Wars franchise, including the prequel films and the Obi-Wan Kenobi TV series.
- 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 Ungar Triple: [Ungar, hasNotableBearer, Michael Ungar]
Generated description
Michael Ungar is a Canadian researcher and author best known for his work on resilience in children, families, and communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Ungar Target entity description: Michael Ungar is a Canadian researcher and author best known for his work on resilience in children, families, and communities.
-
A.
Daniel Shere
Daniel Shere is a screenwriter best known for his work on the animated film "Epic."
-
B.
Richard Leacock
Richard Leacock was a pioneering British-born documentary filmmaker and key figure in the development of cinéma vérité and direct cinema.
-
C.
John Neufeld
John Neufeld is a film music orchestrator known for his work on major Hollywood scores, including Disney’s animated feature "Mulan" (1998).
-
D.
Michael Krupat
Michael Krupat is a television producer best known for creating the popular Food Network cooking competition series "Chopped."
-
E.
Bonnie Piesse
Bonnie Piesse is an Australian actress and singer best known for playing Beru Lars in the Star Wars franchise, including the prequel films and the Obi-Wan Kenobi TV series.
- 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_69d8b913351c8190932b6a426de04b41 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ff79851481909a4bbeb14fb00647 |
completed | April 19, 2026, 4:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03b3f6c1b081908fdd0ccb6f1bf633 |
completed | May 12, 2026, 11:12 p.m. |
| NEDg | Description generation | batch_6a03b4f0cd188190a9577a3999a5b473 |
completed | May 12, 2026, 11:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03b5c09f4881909fb0ead5fc48895c |
completed | May 12, 2026, 11:20 p.m. |
Created at: April 10, 2026, 10:34 a.m.