Triple

T22753539
Position Surface form Disambiguated ID Type / Status
Subject William III of Toulouse E562772 entity
Predicate mother P120 FINISHED
Object Adelaide of Anjou
Adelaide of Anjou was a prominent 10th–11th century French noblewoman and countess whose multiple politically significant marriages linked major dynasties in Anjou, Toulouse, and beyond.
E1851163 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 Anjou | Statement: [William III of Toulouse, mother, Adelaide of Anjou]
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 Anjou
Triple: [William III of Toulouse, mother, Adelaide of Anjou]
Generated description
Adelaide of Anjou was a prominent 10th–11th century French noblewoman and countess whose multiple politically significant marriages linked major dynasties in Anjou, Toulouse, and beyond.

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_69e24551ec7881909a9c924dbea155f6 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f179bb80ac8190b53a1e00704c4ff5 completed April 29, 2026, 3:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a25377b44bc81909ec4c1952d8cfad0 completed June 7, 2026, 9:18 a.m.
NEDg Description generation batch_6a253bdcaf2c8190b24d33e76d6efc78 completed June 7, 2026, 9:37 a.m.
NED2 Entity disambiguation (via description) batch_6a253fd1f0488190abea40d50e953b04 completed June 7, 2026, 9:54 a.m.
Created at: April 17, 2026, 3:25 p.m.