Triple

T28108620
Position Surface form Disambiguated ID Type / Status
Subject Golden Goblet main competition E710427 entity
Predicate awardGiven P287 FINISHED
Object Golden Goblet for Best Cinematography
The Golden Goblet for Best Cinematography is a top honor at the Shanghai International Film Festival recognizing outstanding achievement in visual filmmaking.
E1803568 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: Golden Goblet for Best Cinematography | Statement: [Golden Goblet main competition, awardGiven, Golden Goblet for Best Cinematography]
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: Golden Goblet for Best Cinematography
Triple: [Golden Goblet main competition, awardGiven, Golden Goblet for Best Cinematography]
Generated description
The Golden Goblet for Best Cinematography is a top honor at the Shanghai International Film Festival recognizing outstanding achievement in visual filmmaking.

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_69ef9b71fdb081908b4a61cd7ff147c1 completed April 27, 2026, 5:22 p.m.
NER Named-entity recognition batch_69f640c5d95881908ca569d8395c7986 completed May 2, 2026, 6:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15c92d1d548190bc0027a4f88f667d completed May 26, 2026, 4:24 p.m.
NEDg Description generation batch_6a15ca4a3d548190ac7be49c7d35ee0b completed May 26, 2026, 4:28 p.m.
NED2 Entity disambiguation (via description) batch_6a15cbc2fa6c8190a3d8a4b60ab6104c completed May 26, 2026, 4:35 p.m.
Created at: April 27, 2026, 9:10 p.m.