Triple
T32649724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dark Side of the Gym (music video) |
E834694
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object |
Justin Peck
Justin Peck is an acclaimed American choreographer and dancer, best known for his innovative work with the New York City Ballet and for bringing contemporary flair to both stage and film projects.
|
E217111
|
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: Justin Peck | Statement: [Dark Side of the Gym (music video), director, Justin Peck]
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: Justin Peck Triple: [Dark Side of the Gym (music video), director, Justin Peck]
Generated description
Justin Peck is an acclaimed American choreographer and dancer, best known for his innovative work with the New York City Ballet and for bringing contemporary flair to both stage and film projects.
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_69f3492e773c81908afc10651e46cad3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6c775d6188190b236fc4f89a11b61 |
completed | May 3, 2026, 3:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3492a99a8481909b7afc93f5b9b6d0 |
completed | June 19, 2026, 12:51 a.m. |
| NEDg | Description generation | batch_6a34938a58dc8190ab8e23d0b021db8b |
completed | June 19, 2026, 12:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a34941dcdbc8190b6549f9be8eb672f |
completed | June 19, 2026, 12:58 a.m. |
Created at: May 1, 2026, 1:08 a.m.