Triple
T4560023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mom |
E120569
|
entity |
| Predicate | composer |
P1361
|
FINISHED |
| Object |
Grant Geissman
Grant Geissman is an American jazz and studio guitarist and composer known for his solo work and contributions to television and film scores.
|
E452287
|
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: Grant Geissman | Statement: [Mom, composer, Grant Geissman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grant Geissman Context triple: [Mom, composer, Grant Geissman]
-
A.
Ian Megibben
Ian Megibben is a cinematographer best known for his work on the animated film "Finding Dory."
-
B.
Don Saleski
Don Saleski is a former NHL right winger best known for his gritty, physical play with the Philadelphia Flyers during their 1970s "Broad Street Bullies" era.
-
C.
Jon Bosak
Jon Bosak is a computer scientist best known for leading the original XML specification effort at the World Wide Web Consortium (W3C), which helped standardize data interchange on the web.
-
D.
Ted Cheesman
Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
-
E.
Gene Havlick
Gene Havlick was an American film editor known for his work on numerous classic Hollywood films, including collaborations on major studio productions in the 1930s and 1940s.
- 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: Grant Geissman Triple: [Mom, composer, Grant Geissman]
Generated description
Grant Geissman is an American jazz and studio guitarist and composer known for his solo work and contributions to television and film scores.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Grant Geissman Target entity description: Grant Geissman is an American jazz and studio guitarist and composer known for his solo work and contributions to television and film scores.
-
A.
Ian Megibben
Ian Megibben is a cinematographer best known for his work on the animated film "Finding Dory."
-
B.
Don Saleski
Don Saleski is a former NHL right winger best known for his gritty, physical play with the Philadelphia Flyers during their 1970s "Broad Street Bullies" era.
-
C.
Jon Bosak
Jon Bosak is a computer scientist best known for leading the original XML specification effort at the World Wide Web Consortium (W3C), which helped standardize data interchange on the web.
-
D.
Ted Cheesman
Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
-
E.
Gene Havlick
Gene Havlick was an American film editor known for his work on numerous classic Hollywood films, including collaborations on major studio productions in the 1930s and 1940s.
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd582b871c8190be0b70c76d639000 |
completed | March 20, 2026, 2:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdc593eaf881908a9043366230b391 |
completed | March 20, 2026, 10:09 p.m. |
| NEDg | Description generation | batch_69bdc5f0b52c8190bbfa2a6a22d56725 |
completed | March 20, 2026, 10:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdc63b1e0881908f861f7c9c5ce3ac |
completed | March 20, 2026, 10:12 p.m. |
Created at: March 20, 2026, 1:09 p.m.