Triple

T38353820
Position Surface form Disambiguated ID Type / Status
Subject Joan FitzAlan E1046268 entity
Predicate child P120 FINISHED
Object Anne de Bohun
Anne de Bohun was a medieval English noblewoman, a member of the influential Bohun family and daughter of Joan FitzAlan.
 E2282212 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: Anne de Bohun | Statement: [Joan FitzAlan, child, Anne de Bohun]
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: Anne de Bohun
Triple: [Joan FitzAlan, child, Anne de Bohun]
Generated description
Anne de Bohun was a medieval English noblewoman, a member of the influential Bohun family and daughter of Joan FitzAlan.

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_69f76e3a94fc81908edc175e8d259e80 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69fcc7338120819081cb46547d60f2cb completed May 7, 2026, 5:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a42157eaa2081908fe15dd71f7b9589 completed June 29, 2026, 6:49 a.m.
NEDg Description generation batch_6a4216ecd3688190a117869a30d4c46b completed June 29, 2026, 6:55 a.m.
NED2 Entity disambiguation (via description) batch_6a421746980081908d03f4fafab2c10f completed June 29, 2026, 6:57 a.m.
Created at: May 3, 2026, 4:31 p.m.