Triple

T27212008
Position Surface form Disambiguated ID Type / Status
Subject St. Patrokli Cathedral E684020 entity
Predicate namedAfter P63 FINISHED
Object Saint Patroclus of Troyes
Saint Patroclus of Troyes was a 3rd-century Christian martyr and patron saint venerated particularly in France and Germany.
 E1761364 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: Saint Patroclus of Troyes | Statement: [St. Patrokli Cathedral, namedAfter, Saint Patroclus of Troyes]
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: Saint Patroclus of Troyes
Triple: [St. Patrokli Cathedral, namedAfter, Saint Patroclus of Troyes]
Generated description
Saint Patroclus of Troyes was a 3rd-century Christian martyr and patron saint venerated particularly in France and Germany.

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_69eefad339a08190aeacb2a198f1a39b completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f62619db548190b38c77d51ea79fb3 completed May 2, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1253a3b2b88190856eb94df30c7025 completed May 24, 2026, 1:25 a.m.
NEDg Description generation batch_6a125455f8fc81909ae39b6651a0fdb0 completed May 24, 2026, 1:28 a.m.
NED2 Entity disambiguation (via description) batch_6a12552837d88190a12496ca49423f0f completed May 24, 2026, 1:32 a.m.
Created at: April 27, 2026, 9:40 a.m.