Triple
T10568095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cyril M. Kornbluth |
E249402
|
entity |
| Predicate | usedPseudonym |
P3799
|
FINISHED |
| Object |
Simon Eisner
Simon Eisner is a pseudonym used by American science fiction writer Cyril M. Kornbluth.
|
E871856
|
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: Simon Eisner | Statement: [Cyril M. Kornbluth, usedPseudonym, Simon Eisner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Simon Eisner Context triple: [Cyril M. Kornbluth, usedPseudonym, Simon Eisner]
-
A.
Jon Rubinstein
Jon Rubinstein is an American computer engineer and executive best known for his key role in developing Apple's iPod and later leading Palm as CEO.
-
B.
Sam Greisman
Sam Greisman is an American writer and director known publicly as the son of acclaimed actress Sally Field.
-
C.
Michael Greenberg
Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
-
D.
Michael Greenberg
Michael Greenberg is an American businessman best known as the co-founder and longtime executive leader of the global footwear company Skechers.
-
E.
Michael Moscovitz
Michael Moscovitz is a central character in Meg Cabot’s "The Princess Diaries" series, known as Mia Thermopolis’s witty, musically inclined romantic interest and eventual boyfriend.
- 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: Simon Eisner Triple: [Cyril M. Kornbluth, usedPseudonym, Simon Eisner]
Generated description
Simon Eisner is a pseudonym used by American science fiction writer Cyril M. Kornbluth.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Simon Eisner Target entity description: Simon Eisner is a pseudonym used by American science fiction writer Cyril M. Kornbluth.
-
A.
Jon Rubinstein
Jon Rubinstein is an American computer engineer and executive best known for his key role in developing Apple's iPod and later leading Palm as CEO.
-
B.
Sam Greisman
Sam Greisman is an American writer and director known publicly as the son of acclaimed actress Sally Field.
-
C.
Michael Greenberg
Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
-
D.
Michael Greenberg
Michael Greenberg is an American businessman best known as the co-founder and longtime executive leader of the global footwear company Skechers.
-
E.
Michael Moscovitz
Michael Moscovitz is a central character in Meg Cabot’s "The Princess Diaries" series, known as Mia Thermopolis’s witty, musically inclined romantic interest and eventual boyfriend.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5272ff53c8190ae7c399d49b585f5 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b4c26ec8190910efdf4a236d654 |
completed | April 10, 2026, 7:11 p.m. |
| NEDg | Description generation | batch_69d94e2f16788190bec54b250dad09a9 |
completed | April 10, 2026, 7:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9518517608190b5036694b83f5f58 |
completed | April 10, 2026, 7:37 p.m. |
Created at: April 6, 2026, 12:37 p.m.