Triple

T30017808
Position Surface form Disambiguated ID Type / Status
Subject Red Carpet Green Dress E762643 entity
Predicate associatedWithEvent P37 FINISHED
Object Oscars red carpet
The Oscars red carpet is the high-profile arrival area at the Academy Awards where celebrities are photographed and interviewed in glamorous, often statement-making fashion.
 E331104 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: Oscars red carpet | Statement: [Red Carpet Green Dress, associatedWithEvent, Oscars red carpet]
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: Oscars red carpet
Triple: [Red Carpet Green Dress, associatedWithEvent, Oscars red carpet]
Generated description
The Oscars red carpet is the high-profile arrival area at the Academy Awards where celebrities are photographed and interviewed in glamorous, often statement-making fashion.

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_69f2246b0c84819094f1250b6a02d277 completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f67986150481908b1ee295d00aef3b completed May 2, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27221520108190ba5a553bbf336075 completed June 8, 2026, 8:12 p.m.
NEDg Description generation batch_6a272388d9c48190a4ed6758fa181b41 completed June 8, 2026, 8:18 p.m.
NED2 Entity disambiguation (via description) batch_6a27245326ac8190a1d398727fb54bc8 completed June 8, 2026, 8:21 p.m.
Created at: April 29, 2026, 6:46 p.m.