Triple

T25696353
Position Surface form Disambiguated ID Type / Status
Subject Jeddah Derby E644330 entity
Predicate typicalVenue P373 FINISHED
Object Prince Abdullah Al Faisal Stadium
Prince Abdullah Al Faisal Stadium is a major multi-purpose sports venue in Jeddah, Saudi Arabia, best known for hosting high-profile football matches and local derbies.
E1692507 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: Prince Abdullah Al Faisal Stadium | Statement: [Jeddah Derby, typicalVenue, Prince Abdullah Al Faisal Stadium]
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: Prince Abdullah Al Faisal Stadium
Triple: [Jeddah Derby, typicalVenue, Prince Abdullah Al Faisal Stadium]
Generated description
Prince Abdullah Al Faisal Stadium is a major multi-purpose sports venue in Jeddah, Saudi Arabia, best known for hosting high-profile football matches and local derbies.

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_69e77e82c9bc8190893090b2f6c64f1d completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fbc4f1e88190bd5b195d92e44d3e completed May 2, 2026, 1:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10cbf3ef9c81909929698f9cde6173 completed May 22, 2026, 9:34 p.m.
NEDg Description generation batch_6a10cc81bb8881909413a1b8924a0fe2 completed May 22, 2026, 9:37 p.m.
NED2 Entity disambiguation (via description) batch_6a10cd0fbcc08190a12ded88d999feab completed May 22, 2026, 9:39 p.m.
Created at: April 21, 2026, 8:36 p.m.