Triple

T35825350
Position Surface form Disambiguated ID Type / Status
Subject Nicholas II of Opava E1035626 entity
Predicate spouse P13 FINISHED
Object Jutta of Opole
Jutta of Opole was a medieval Silesian duchess from the Piast dynasty who became consort to Nicholas II of Opava.
E2186674 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: Jutta of Opole | Statement: [Nicholas II of Opava, spouse, Jutta of Opole]
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: Jutta of Opole
Triple: [Nicholas II of Opava, spouse, Jutta of Opole]
Generated description
Jutta of Opole was a medieval Silesian duchess from the Piast dynasty who became consort to Nicholas II of Opava.

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_69f76e185ffc8190880b3cdf51decd38 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a902f7a481909819b583bccd4c8f completed May 3, 2026, 7:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39dbb359fc8190a88dbd1cf0cc0bd7 completed June 23, 2026, 1:04 a.m.
NEDg Description generation batch_6a39dc6faffc8190bc65e812b89dffda completed June 23, 2026, 1:07 a.m.
NED2 Entity disambiguation (via description) batch_6a39dd3627f48190a70cd2c7a8497aa9 completed June 23, 2026, 1:11 a.m.
Created at: May 3, 2026, 4:06 p.m.