Triple
T14445386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weill |
E358191
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Peter Weill
Peter Weill is an Australian-born academic and author best known for his influential work on IT governance and digital business strategy, particularly at the MIT Sloan School of Management.
|
E1107327
|
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: Peter Weill | Statement: [Weill, hasNotableBearer, Peter Weill]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Weill Context triple: [Weill, hasNotableBearer, Peter Weill]
-
A.
Martin Weil
Martin Weil is a journalist and writer best known for his long career as a reporter and editor at The Washington Post.
-
B.
Paul Weill
Paul Weill is an individual notable enough to be recognized as a bearer of the surname Weill, though specific widely known biographical details about him are not well documented.
-
C.
Philip Steuer
Philip Steuer is a film producer best known for his work on major studio projects, including the Disney drama "Saving Mr. Banks."
-
D.
Philip Liebmann
Philip Liebmann was the husband of American film actress Linda Darnell.
-
E.
Michael Lehmann
Michael Lehmann is an American film and television director best known for the dark comedy "Heathers" and various other Hollywood comedies.
- 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: Peter Weill Triple: [Weill, hasNotableBearer, Peter Weill]
Generated description
Peter Weill is an Australian-born academic and author best known for his influential work on IT governance and digital business strategy, particularly at the MIT Sloan School of Management.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peter Weill Target entity description: Peter Weill is an Australian-born academic and author best known for his influential work on IT governance and digital business strategy, particularly at the MIT Sloan School of Management.
-
A.
Martin Weil
Martin Weil is a journalist and writer best known for his long career as a reporter and editor at The Washington Post.
-
B.
Paul Weill
Paul Weill is an individual notable enough to be recognized as a bearer of the surname Weill, though specific widely known biographical details about him are not well documented.
-
C.
Philip Steuer
Philip Steuer is a film producer best known for his work on major studio projects, including the Disney drama "Saving Mr. Banks."
-
D.
Philip Liebmann
Philip Liebmann was the husband of American film actress Linda Darnell.
-
E.
Michael Lehmann
Michael Lehmann is an American film and television director best known for the dark comedy "Heathers" and various other Hollywood comedies.
- 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_69d82794dfa081909b9134ad2e32244b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de915e76f481909fe9462f964b5b1c |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8aa904c08190b33796b832aa100f |
completed | May 8, 2026, 7:03 a.m. |
| NEDg | Description generation | batch_69fd8c9564a08190bfacd7ba9cadb6b6 |
completed | May 8, 2026, 7:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd8d1414f88190b6cea5a7106f1c3c |
completed | May 8, 2026, 7:13 a.m. |
Created at: April 10, 2026, 1:19 a.m.