Triple

T26304752
Position Surface form Disambiguated ID Type / Status
Subject Verden Cathedral E661649 entity
Predicate localLanguageName P52154 FINISHED
Object Dom zu Verden
Dom zu Verden is a historic medieval cathedral in Verden (Aller), Germany, notable for its Gothic architecture and former status as the seat of the Prince-Bishopric of Verden.
E1717742 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: Dom zu Verden | Statement: [Verden Cathedral, localLanguageName, Dom zu Verden]
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: Dom zu Verden
Triple: [Verden Cathedral, localLanguageName, Dom zu Verden]
Generated description
Dom zu Verden is a historic medieval cathedral in Verden (Aller), Germany, notable for its Gothic architecture and former status as the seat of the Prince-Bishopric of Verden.

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_69ee812dacfc81908484aade9120fba9 completed April 26, 2026, 9:18 p.m.
NER Named-entity recognition batch_69f60ee22a908190a62640e48c2e7659 completed May 2, 2026, 2:49 p.m.
NED1 Entity disambiguation (via context triple) batch_6a118fce78488190a441368f0700b817 completed May 23, 2026, 11:30 a.m.
NEDg Description generation batch_6a1190713f4c819082a89700881a3c46 completed May 23, 2026, 11:33 a.m.
NED2 Entity disambiguation (via description) batch_6a119145a7008190b6b01851f1ee63ad completed May 23, 2026, 11:36 a.m.
Created at: April 26, 2026, 10:18 p.m.