Triple
T8482638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jumper et al., Nature 2021 |
E200555
|
entity |
| Predicate | hasAuthor |
P4244
|
FINISHED |
| Object |
Michael Figurnov
Michael Figurnov is a researcher and scientist known for co-authoring high-impact work published in the journal Nature.
|
E736798
|
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 Figurnov | Statement: [Jumper et al., Nature 2021, hasAuthor, Michael Figurnov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Figurnov Context triple: [Jumper et al., Nature 2021, hasAuthor, Michael Figurnov]
-
A.
Max Zaritsky
Max Zaritsky was an American labor leader and union organizer who played a key role in the early development of industrial unionism in the United States.
-
B.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
C.
Kirill Shubsky
Kirill Shubsky is a Russian businessman known primarily as the husband of actress and model Anastasia Shubskaya.
-
D.
Jason Fuchs
Jason Fuchs is an American screenwriter and actor best known for writing major studio films such as Wonder Woman (2017) and Pan (2015).
-
E.
Jonathan Pytko
Jonathan Pytko is a cinematographer best known for his work on the Pixar animated film "Turning Red."
- 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 Figurnov Triple: [Jumper et al., Nature 2021, hasAuthor, Michael Figurnov]
Generated description
Michael Figurnov is a researcher and scientist known for co-authoring high-impact work published in the journal Nature.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Figurnov Target entity description: Michael Figurnov is a researcher and scientist known for co-authoring high-impact work published in the journal Nature.
-
A.
Max Zaritsky
Max Zaritsky was an American labor leader and union organizer who played a key role in the early development of industrial unionism in the United States.
-
B.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
C.
Kirill Shubsky
Kirill Shubsky is a Russian businessman known primarily as the husband of actress and model Anastasia Shubskaya.
-
D.
Jason Fuchs
Jason Fuchs is an American screenwriter and actor best known for writing major studio films such as Wonder Woman (2017) and Pan (2015).
-
E.
Jonathan Pytko
Jonathan Pytko is a cinematographer best known for his work on the Pixar animated film "Turning Red."
- 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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe53638c48190b742fc51d1b4442a |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a2b2e9081909f19712946c6ec20 |
completed | April 2, 2026, 9:43 a.m. |
| NEDg | Description generation | batch_69ce3b4008a0819096bb44b46f510213 |
completed | April 2, 2026, 9:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce3c000e608190adf1b6499d382529 |
completed | April 2, 2026, 9:50 a.m. |
Created at: March 30, 2026, 6:12 p.m.