Triple

T31041701
Position Surface form Disambiguated ID Type / Status
Subject Henry I, Duke of Brabant E791011 entity
Predicate child P120 FINISHED
Object Adelaide of Brabant
Adelaide of Brabant was a 13th-century noblewoman from the ducal house of Brabant who became Countess of Boulogne through marriage and played a role in the politics of the Low Countries and northern France.
E2196037 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: Adelaide of Brabant | Statement: [Henry I, Duke of Brabant, child, Adelaide of Brabant]
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: Adelaide of Brabant
Triple: [Henry I, Duke of Brabant, child, Adelaide of Brabant]
Generated description
Adelaide of Brabant was a 13th-century noblewoman from the ducal house of Brabant who became Countess of Boulogne through marriage and played a role in the politics of the Low Countries and northern France.

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_69f224ca2fa881908a3ac5fedf207b90 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f694fa62308190aa7ca49724a6cb06 completed May 3, 2026, 12:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3a37fe85c48190a3e652d2543268ad completed June 23, 2026, 7:38 a.m.
NEDg Description generation batch_6a3a388b449c8190b3ded5a3a91dacfa completed June 23, 2026, 7:40 a.m.
NED2 Entity disambiguation (via description) batch_6a3a414e220c819093a0f0611a24df52 completed June 23, 2026, 8:18 a.m.
Created at: April 29, 2026, 8:59 p.m.