Triple

T37746875
Position Surface form Disambiguated ID Type / Status
Subject Anne Burrell E940867 entity
Predicate notableWork P4 FINISHED
Object Cook Like a Rock Star
Cook Like a Rock Star is a cookbook by celebrity chef Anne Burrell that teaches home cooks restaurant-style techniques and bold, flavorful recipes in an accessible way.
 E2240570 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: Cook Like a Rock Star | Statement: [Anne Burrell, notableWork, Cook Like a Rock Star]
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: Cook Like a Rock Star
Triple: [Anne Burrell, notableWork, Cook Like a Rock Star]
Generated description
Cook Like a Rock Star is a cookbook by celebrity chef Anne Burrell that teaches home cooks restaurant-style techniques and bold, flavorful recipes in an accessible way.

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_69f76ee0e32c8190b40a3b4cf590337c completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fbaec2fa8881909792a907522bf147 completed May 6, 2026, 9:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40d68f9f2081909e22dbb3659eed0b completed June 28, 2026, 8:08 a.m.
NEDg Description generation batch_6a40d95e44b4819092cde3aae2e1c371 completed June 28, 2026, 8:20 a.m.
NED2 Entity disambiguation (via description) batch_6a40d9bb08a88190b973454e0bbbe7ba completed June 28, 2026, 8:22 a.m.
Created at: May 3, 2026, 4:19 p.m.