Triple

T26559603
Position Surface form Disambiguated ID Type / Status
Subject It's a Laugh Productions E666201 entity
Predicate notableWork P4 FINISHED
Object Sydney to the Max
Sydney to the Max is a Disney Channel live-action family sitcom that follows a middle school girl and her single dad through parallel timelines of their preteen years.
 E1732524 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: Sydney to the Max | Statement: [It's a Laugh Productions, notableWork, Sydney to the Max]
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: Sydney to the Max
Triple: [It's a Laugh Productions, notableWork, Sydney to the Max]
Generated description
Sydney to the Max is a Disney Channel live-action family sitcom that follows a middle school girl and her single dad through parallel timelines of their preteen years.

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_69ee9cf7e94481909f0d556b36e43572 completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f6146a78bc81908ff10eca1fe217b0 completed May 2, 2026, 3:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c823f96081909faab64a99d2483b completed May 23, 2026, 3:30 p.m.
NEDg Description generation batch_6a11c919c3d08190ae5cc3a21f5257be completed May 23, 2026, 3:34 p.m.
NED2 Entity disambiguation (via description) batch_6a11ca2243988190a158631f4b94e205 completed May 23, 2026, 3:39 p.m.
Created at: April 27, 2026, 1:51 a.m.