Triple
T10836719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Intelligence |
E255777
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Michael L. Sale
Michael L. Sale is an editor known for his work on the publication "Central Intelligence."
|
E891644
|
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 L. Sale | Statement: [Central Intelligence, editedBy, Michael L. Sale]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael L. Sale Context triple: [Central Intelligence, editedBy, Michael L. Sale]
-
A.
Michael L. Sale
Michael L. Sale is a film editor known for his work on major Hollywood comedies, including the hit movie "Bridesmaids."
-
B.
Michael J. Weithorn
Michael J. Weithorn is an American television writer and producer best known for creating and working on several sitcoms, including "Ned and Stacey" and "The King of Queens."
-
C.
Michael W. Burns
Michael W. Burns is an actor known for his role in the Western television miniseries "Broken Trail."
-
D.
Eric A. Sears
Eric A. Sears is a film editor known for his work on the 2015 horror-comedy movie "Krampus."
-
E.
Stephen F. Martin
Stephen F. Martin is an American organic chemist renowned for his influential research in synthetic methodology and complex molecule synthesis.
- 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 L. Sale Triple: [Central Intelligence, editedBy, Michael L. Sale]
Generated description
Michael L. Sale is an editor known for his work on the publication "Central Intelligence."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael L. Sale Target entity description: Michael L. Sale is an editor known for his work on the publication "Central Intelligence."
-
A.
Michael L. Sale
Michael L. Sale is a film editor known for his work on major Hollywood comedies, including the hit movie "Bridesmaids."
-
B.
Michael J. Weithorn
Michael J. Weithorn is an American television writer and producer best known for creating and working on several sitcoms, including "Ned and Stacey" and "The King of Queens."
-
C.
Michael W. Burns
Michael W. Burns is an actor known for his role in the Western television miniseries "Broken Trail."
-
D.
Eric A. Sears
Eric A. Sears is a film editor known for his work on the 2015 horror-comedy movie "Krampus."
-
E.
Stephen F. Martin
Stephen F. Martin is an American organic chemist renowned for his influential research in synthetic methodology and complex molecule synthesis.
- 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d746ff70148190b844ab92d796af6c |
completed | April 9, 2026, 6:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e154a582188190af96ae0d5cc08dc4 |
completed | April 16, 2026, 9:29 p.m. |
| NEDg | Description generation | batch_69e1739ed9b88190949125759f42efd2 |
completed | April 16, 2026, 11:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e175ecef0c8190b08052d751f1a608 |
completed | April 16, 2026, 11:51 p.m. |
Created at: April 8, 2026, 9:19 p.m.