Triple

T28419056
Position Surface form Disambiguated ID Type / Status
Subject Nazanin Afshin-Jam E719889 entity
Predicate participantIn P149 FINISHED
Object Miss World 2003 pageant
The Miss World 2003 pageant was an international beauty contest held in Sanya, China, where contestants from around the globe competed for the Miss World title.
 E1817899 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: Miss World 2003 pageant | Statement: [Nazanin Afshin-Jam, participantIn, Miss World 2003 pageant]
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: Miss World 2003 pageant
Triple: [Nazanin Afshin-Jam, participantIn, Miss World 2003 pageant]
Generated description
The Miss World 2003 pageant was an international beauty contest held in Sanya, China, where contestants from around the globe competed for the Miss World title.

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_69eff6f1c5088190bc24bfbf92f9c017 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64dc3ba8c81909c5ccae79102cde7 completed May 2, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a16331bd37c8190b5d58b33a47922b6 completed May 26, 2026, 11:56 p.m.
NEDg Description generation batch_6a16373dedd08190967ca5c270575a79 completed May 27, 2026, 12:13 a.m.
NED2 Entity disambiguation (via description) batch_6a1637e4d0548190b8d4c7a90580032d completed May 27, 2026, 12:16 a.m.
Created at: April 28, 2026, 1:32 a.m.