Triple
T29953918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Street Fighter: The Legend of Chun-Li |
E760842
|
entity |
| Predicate | cinematographer |
P1953
|
FINISHED |
| Object |
Geoff Boyle
Geoff Boyle is a British cinematographer known for his work on feature films, television, and commercials, as well as for founding the Cinematography Mailing List (CML), an influential online community for professional cinematographers.
|
E1950195
|
NE FINISHED |
How this triple was built (2 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: Geoff Boyle | Statement: [Street Fighter: The Legend of Chun-Li, cinematographer, Geoff Boyle]
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: Geoff Boyle Triple: [Street Fighter: The Legend of Chun-Li, cinematographer, Geoff Boyle]
Generated description
Geoff Boyle is a British cinematographer known for his work on feature films, television, and commercials, as well as for founding the Cinematography Mailing List (CML), an influential online community for professional cinematographers.
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_69f2246562b881909d57622f4086d43d |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6783880608190906379178b865dc0 |
completed | May 2, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2946fa92748190a05e6f5f6d6d9f68 |
completed | June 10, 2026, 11:14 a.m. |
| NEDg | Description generation | batch_6a294edd87888190a40f71d4d7f57b18 |
completed | June 10, 2026, 11:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2950ac30e88190a3f55d5a68f317d8 |
completed | June 10, 2026, 11:55 a.m. |
Created at: April 29, 2026, 6:26 p.m.