Triple

T29407560
Position Surface form Disambiguated ID Type / Status
Subject Fedora Design Team E745809 entity
Predicate collaboratesWith P37 FINISHED
Object Fedora Websites Team
The Fedora Websites Team is a group within the Fedora Project responsible for designing, developing, and maintaining Fedora’s official web presence and related online resources.
E1865983 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: Fedora Websites Team | Statement: [Fedora Design Team, collaboratesWith, Fedora Websites Team]
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: Fedora Websites Team
Triple: [Fedora Design Team, collaboratesWith, Fedora Websites Team]
Generated description
The Fedora Websites Team is a group within the Fedora Project responsible for designing, developing, and maintaining Fedora’s official web presence and related online resources.

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_69f0a79eb7d081908c67197a5f347e68 completed April 28, 2026, 12:27 p.m.
NER Named-entity recognition batch_69f66a35247c8190acfba4d40db8c210 completed May 2, 2026, 9:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25d91c73348190ac34263384ce635c completed June 7, 2026, 8:48 p.m.
NEDg Description generation batch_6a25db1025b88190956b161e170f38dc completed June 7, 2026, 8:56 p.m.
NED2 Entity disambiguation (via description) batch_6a25decd764081909cf8136e27bd6586 completed June 7, 2026, 9:12 p.m.
Created at: April 28, 2026, 2:55 p.m.