Triple
T10158542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Usenet |
E233830
|
entity |
| Predicate | hasCreator |
P806
|
FINISHED |
| Object |
Jim Ellis
Jim Ellis was a pioneering computer scientist best known as one of the creators of Usenet, an early global distributed discussion system that predated and influenced modern internet forums.
|
E845111
|
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: Jim Ellis | Statement: [Usenet, hasCreator, Jim Ellis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jim Ellis Context triple: [Usenet, hasCreator, Jim Ellis]
-
A.
Joe Ellis
Joe Ellis is an American sports executive best known as the longtime president and CEO of the Denver Broncos in the National Football League.
-
B.
Mark Ellam
Mark Ellam is a cinematographer known for his work on the film "The Take."
-
C.
Len Elmore
Len Elmore is a former American professional basketball player, college basketball star at the University of Maryland, and longtime television sports analyst and legal analyst.
-
D.
Sam Ellis
Sam Ellis is a music producer known for his work on Taylor Swift’s self-titled debut album.
-
E.
Steven Elliott
Steven Elliott is an actor known for his role in the National Theatre’s acclaimed stage production of "Frankenstein."
- 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: Jim Ellis Triple: [Usenet, hasCreator, Jim Ellis]
Generated description
Jim Ellis was a pioneering computer scientist best known as one of the creators of Usenet, an early global distributed discussion system that predated and influenced modern internet forums.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jim Ellis Target entity description: Jim Ellis was a pioneering computer scientist best known as one of the creators of Usenet, an early global distributed discussion system that predated and influenced modern internet forums.
-
A.
Joe Ellis
Joe Ellis is an American sports executive best known as the longtime president and CEO of the Denver Broncos in the National Football League.
-
B.
Mark Ellam
Mark Ellam is a cinematographer known for his work on the film "The Take."
-
C.
Len Elmore
Len Elmore is a former American professional basketball player, college basketball star at the University of Maryland, and longtime television sports analyst and legal analyst.
-
D.
Sam Ellis
Sam Ellis is a music producer known for his work on Taylor Swift’s self-titled debut album.
-
E.
Steven Elliott
Steven Elliott is an actor known for his role in the National Theatre’s acclaimed stage production of "Frankenstein."
- 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_69ca848e80748190b91d1e04d35512c7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec5507f08190b47f797bacd5640c |
completed | April 2, 2026, 4:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d300b7b3108190a8e7581193c322be |
completed | April 6, 2026, 12:39 a.m. |
| NEDg | Description generation | batch_69d302537a548190b211727dd124cba6 |
completed | April 6, 2026, 12:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d3033245448190bcc802b7dbd274fc |
completed | April 6, 2026, 12:49 a.m. |
Created at: March 30, 2026, 9:09 p.m.