Triple
T8472134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Symbolics |
E200304
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Nick Gall
Nick Gall is a computer scientist and entrepreneur best known as one of the founders of the pioneering Lisp machine company Symbolics.
|
E736608
|
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: Nick Gall | Statement: [Symbolics, foundedBy, Nick Gall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nick Gall Context triple: [Symbolics, foundedBy, Nick Gall]
-
A.
Nick Wells
Nick Wells is the seasoned, meticulous safecracker and professional thief portrayed by Robert De Niro in the 2001 heist film "The Score."
-
B.
Nick Barton
Nick Barton is a prominent evolutionary biologist known for his influential work on the genetics of adaptation and speciation.
-
C.
Nick Roud
Nick Roud is an actor known for his role in the film "Finding Neverland."
-
D.
Nick Black
Nick Black is a prominent British health services researcher and academic known for his contributions to evaluating and improving healthcare systems and policy.
-
E.
Nick Sauer
Nick Sauer is an American politician who served as a Republican member of the Illinois House of Representatives before resigning amid allegations of misconduct.
- 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: Nick Gall Triple: [Symbolics, foundedBy, Nick Gall]
Generated description
Nick Gall is a computer scientist and entrepreneur best known as one of the founders of the pioneering Lisp machine company Symbolics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nick Gall Target entity description: Nick Gall is a computer scientist and entrepreneur best known as one of the founders of the pioneering Lisp machine company Symbolics.
-
A.
Nick Wells
Nick Wells is the seasoned, meticulous safecracker and professional thief portrayed by Robert De Niro in the 2001 heist film "The Score."
-
B.
Nick Barton
Nick Barton is a prominent evolutionary biologist known for his influential work on the genetics of adaptation and speciation.
-
C.
Nick Roud
Nick Roud is an actor known for his role in the film "Finding Neverland."
-
D.
Nick Black
Nick Black is a prominent British health services researcher and academic known for his contributions to evaluating and improving healthcare systems and policy.
-
E.
Nick Sauer
Nick Sauer is an American politician who served as a Republican member of the Illinois House of Representatives before resigning amid allegations of misconduct.
- 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_69ca831a4f348190bfdd09250e86ae35 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4f3a81881908f20514579945ffa |
completed | March 31, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce39fc9bf481908e37919b13465d18 |
completed | April 2, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_69ce3b1e188c8190ad894478141f6501 |
completed | April 2, 2026, 9:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce3bfd00948190b3956be3f8c5d547 |
completed | April 2, 2026, 9:50 a.m. |
Created at: March 30, 2026, 6:11 p.m.