Triple
T6031067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amazing Stories |
E134302
|
entity |
| Predicate | notableEditor |
P1932
|
FINISHED |
| Object |
Kim Mohan
Kim Mohan was an American editor and game designer best known for his influential work on Dungeons & Dragons and other role-playing game publications.
|
E563060
|
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: Kim Mohan | Statement: [Amazing Stories, notableEditor, Kim Mohan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Mohan Context triple: [Amazing Stories, notableEditor, Kim Mohan]
-
A.
Pradip Krishen
Pradip Krishen is an Indian filmmaker-turned-environmentalist and naturalist known for his documentaries and influential work on urban ecology and tree mapping in India.
-
B.
Suresh Ayyar
Suresh Ayyar is an editor known for his work on the acclaimed Australian memoir "Romulus, My Father."
-
C.
Ashok Kumar
Ashok Kumar was a pioneering and acclaimed Indian film actor, often regarded as one of the first superstars of Hindi cinema.
-
D.
Ashok Saraf
Ashok Saraf is a veteran Indian actor and comedian best known for his prolific work in Marathi films and theatre, as well as memorable roles in Hindi cinema and television.
-
E.
Neal Mohan
Neal Mohan is an Indian-American technology executive and digital advertising expert who serves as the CEO of YouTube.
- 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: Kim Mohan Triple: [Amazing Stories, notableEditor, Kim Mohan]
Generated description
Kim Mohan was an American editor and game designer best known for his influential work on Dungeons & Dragons and other role-playing game publications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kim Mohan Target entity description: Kim Mohan was an American editor and game designer best known for his influential work on Dungeons & Dragons and other role-playing game publications.
-
A.
Pradip Krishen
Pradip Krishen is an Indian filmmaker-turned-environmentalist and naturalist known for his documentaries and influential work on urban ecology and tree mapping in India.
-
B.
Suresh Ayyar
Suresh Ayyar is an editor known for his work on the acclaimed Australian memoir "Romulus, My Father."
-
C.
Ashok Kumar
Ashok Kumar was a pioneering and acclaimed Indian film actor, often regarded as one of the first superstars of Hindi cinema.
-
D.
Ashok Saraf
Ashok Saraf is a veteran Indian actor and comedian best known for his prolific work in Marathi films and theatre, as well as memorable roles in Hindi cinema and television.
-
E.
Neal Mohan
Neal Mohan is an Indian-American technology executive and digital advertising expert who serves as the CEO of YouTube.
- 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_69c0087515148190a97475d412563865 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056af89e881909652957f94317684 |
completed | March 22, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11381aa0c81909487a5eb510c9b41 |
completed | March 23, 2026, 10:18 a.m. |
| NEDg | Description generation | batch_69c113de5d788190affa46cf416d180d |
completed | March 23, 2026, 10:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1145180008190844d91782929349f |
completed | March 23, 2026, 10:22 a.m. |
Created at: March 22, 2026, 4:08 p.m.