Triple

T34930243
Position Surface form Disambiguated ID Type / Status
Subject FIFA U-17 World Cup 2007 E1007410 entity
Predicate nextEdition P2686 FINISHED
Object FIFA U-17 World Cup 2009
The FIFA U-17 World Cup 2009 was the 13th edition of FIFA’s international youth football championship for under-17 national teams, held in Nigeria.
E2120889 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: FIFA U-17 World Cup 2009 | Statement: [FIFA U-17 World Cup 2007, nextEdition, FIFA U-17 World Cup 2009]
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: FIFA U-17 World Cup 2009
Triple: [FIFA U-17 World Cup 2007, nextEdition, FIFA U-17 World Cup 2009]
Generated description
The FIFA U-17 World Cup 2009 was the 13th edition of FIFA’s international youth football championship for under-17 national teams, held in Nigeria.

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_69f76dc3d83881909d5c3c14455cfa2c completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78254e528819083e826605127551a completed May 3, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37b26447cc8190a75dcc83b3110ef5 completed June 21, 2026, 9:44 a.m.
NEDg Description generation batch_6a37b3c955c88190989d0c2404a72ed1 completed June 21, 2026, 9:50 a.m.
NED2 Entity disambiguation (via description) batch_6a37b51173808190a8314a275b7c87d7 completed June 21, 2026, 9:55 a.m.
Created at: May 3, 2026, 4 p.m.