Triple

T35866376
Position Surface form Disambiguated ID Type / Status
Subject William de Corbeil E1037099 entity
Predicate consecratedBy P3357 FINISHED
Object Cardinal John of Crema
Cardinal John of Crema was a 12th-century Italian papal legate and churchman active in England, known for his involvement in ecclesiastical politics and reform.
E2160265 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: Cardinal John of Crema | Statement: [William de Corbeil, consecratedBy, Cardinal John of Crema]
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: Cardinal John of Crema
Triple: [William de Corbeil, consecratedBy, Cardinal John of Crema]
Generated description
Cardinal John of Crema was a 12th-century Italian papal legate and churchman active in England, known for his involvement in ecclesiastical politics and reform.

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_69f76e1d279c8190843e5b64a0a12c3f completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a9c6a3748190892e120272fdafe6 completed May 3, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38a4eb16d4819084e7c6951ab357b7 completed June 22, 2026, 2:58 a.m.
NEDg Description generation batch_6a38a59a0184819080e951c76a48eb0c completed June 22, 2026, 3:01 a.m.
NED2 Entity disambiguation (via description) batch_6a38a63caecc8190a4ac4eb8af4bb18b completed June 22, 2026, 3:04 a.m.
Created at: May 3, 2026, 4:06 p.m.