Triple

T24656808
Position Surface form Disambiguated ID Type / Status
Subject Rainer Maria Woelki E610414 entity
Predicate consecratedBy P3357 FINISHED
Object Joachim Meisner
Joachim Meisner was a prominent German cardinal of the Roman Catholic Church who served as Archbishop of Cologne and was known for his conservative theological stance.
E1648510 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: Joachim Meisner | Statement: [Rainer Maria Woelki, consecratedBy, Joachim Meisner]
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: Joachim Meisner
Triple: [Rainer Maria Woelki, consecratedBy, Joachim Meisner]
Generated description
Joachim Meisner was a prominent German cardinal of the Roman Catholic Church who served as Archbishop of Cologne and was known for his conservative theological stance.

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_69e2c4d453248190a020354e93ef6282 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40f9612b48190909dc8a6064a8f08 completed May 1, 2026, 2:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100ff6652881909e5e4b13ea482aaf completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136992b481909ee04d5c09867f21 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10140b2fec8190aa6d805f54926b56 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 2:34 a.m.