Triple
T13984945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sims |
E336413
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Michael Sims
Michael Sims is an American author and essayist known for his works on nature, science, and cultural history.
|
E1076248
|
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 Sims | Statement: [Sims, hasNotableBearer, Michael Sims]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Sims Context triple: [Sims, hasNotableBearer, Michael Sims]
-
A.
Jay Simms
Jay Simms was an American screenwriter best known for his work on mid-20th-century science fiction and genre films.
-
B.
Brian Sims
Brian Sims is an American civil rights attorney, LGBTQ+ activist, and former Pennsylvania state legislator known for being the first openly gay elected state representative in Pennsylvania.
-
C.
Jack Simms
Jack Simms was the discoverer of Mark Twain Cave, a famous show cave in Missouri associated with author Mark Twain and his writings.
-
D.
David Simas
David Simas is an American political strategist and former Obama White House official who serves as a top executive leader at the Obama Foundation.
-
E.
John Simmons
John Simmons was a 19th-century American clothing manufacturer and philanthropist whose endowment led to the creation of Simmons University in Boston.
- 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 Sims Triple: [Sims, hasNotableBearer, Michael Sims]
Generated description
Michael Sims is an American author and essayist known for his works on nature, science, and cultural history.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Sims Target entity description: Michael Sims is an American author and essayist known for his works on nature, science, and cultural history.
-
A.
Jay Simms
Jay Simms was an American screenwriter best known for his work on mid-20th-century science fiction and genre films.
-
B.
Brian Sims
Brian Sims is an American civil rights attorney, LGBTQ+ activist, and former Pennsylvania state legislator known for being the first openly gay elected state representative in Pennsylvania.
-
C.
Jack Simms
Jack Simms was the discoverer of Mark Twain Cave, a famous show cave in Missouri associated with author Mark Twain and his writings.
-
D.
David Simas
David Simas is an American political strategist and former Obama White House official who serves as a top executive leader at the Obama Foundation.
-
E.
John Simmons
John Simmons was an American statesman who represented South Carolina as a delegate to the Continental Congress during the Revolutionary era.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ea3e5a081908ed8ead108139252 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc32593e08190a1fe8466705c7fe8 |
completed | May 6, 2026, 10:39 p.m. |
| NEDg | Description generation | batch_69fc4348617881908262390a447ad7af |
completed | May 7, 2026, 7:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fc446397988190bb0e415680312ac0 |
completed | May 7, 2026, 7:50 a.m. |
Created at: April 9, 2026, 10:18 p.m.