Triple

T24136362
Position Surface form Disambiguated ID Type / Status
Subject Judith of Flanders E598099 entity
Predicate title P38 FINISHED
Object Queen of the West Saxons
Queen of the West Saxons was the royal consort of the West Saxon (later English) king, holding a prestigious position in early medieval Anglo-Saxon monarchy.
E1621515 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: Queen of the West Saxons | Statement: [Judith of Flanders, title, Queen of the West Saxons]
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: Queen of the West Saxons
Triple: [Judith of Flanders, title, Queen of the West Saxons]
Generated description
Queen of the West Saxons was the royal consort of the West Saxon (later English) king, holding a prestigious position in early medieval Anglo-Saxon monarchy.

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_69e288c92e448190ac57034fa0c863ce completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1df7c3ce08190bcbd9056a6630c2f completed April 29, 2026, 10:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fad23854c8190b71971401d3cb42a completed May 22, 2026, 1:10 a.m.
NEDg Description generation batch_6a0fae49d6a08190b20305c2e8199b80 completed May 22, 2026, 1:15 a.m.
NED2 Entity disambiguation (via description) batch_6a0faf52f7508190ba5ca0d7123f6619 completed May 22, 2026, 1:20 a.m.
Created at: April 17, 2026, 11:27 p.m.