Triple

T28271978
Position Surface form Disambiguated ID Type / Status
Subject Disney Fashion (Disney Village) E712881 entity
Predicate theme P261 FINISHED
Object Disney films
Disney films are a vast collection of animated and live-action movies produced by The Walt Disney Company, renowned worldwide for their family-friendly storytelling, memorable characters, and influential role in popular culture.
E179395 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: Disney films | Statement: [Disney Fashion (Disney Village), theme, Disney films]
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: Disney films
Triple: [Disney Fashion (Disney Village), theme, Disney films]
Generated description
Disney films are a vast collection of animated and live-action movies produced by The Walt Disney Company, renowned worldwide for their family-friendly storytelling, memorable characters, and influential role in popular culture.

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_69efb5216c6881908020dce4aea65381 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f64447b738819088589cca5312c4e8 completed May 2, 2026, 6:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1607203894819099bd7f27f3344def completed May 26, 2026, 8:48 p.m.
NEDg Description generation batch_6a161361ba748190b59b1155e7f27b98 completed May 26, 2026, 9:40 p.m.
NED2 Entity disambiguation (via description) batch_6a1614891498819096109f9a10904797 completed May 26, 2026, 9:45 p.m.
Created at: April 27, 2026, 11:18 p.m.