Triple

T35077129
Position Surface form Disambiguated ID Type / Status
Subject Ingeborg of Denmark, Queen of Norway E1012335 entity
Predicate child P120 FINISHED
Object Agnes Magnusdatter of Norway
Agnes Magnusdatter of Norway was a medieval Norwegian princess, the daughter of King Magnus VI and Queen Ingeborg of Denmark.
E2125134 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: Agnes Magnusdatter of Norway | Statement: [Ingeborg of Denmark, Queen of Norway, child, Agnes Magnusdatter of Norway]
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: Agnes Magnusdatter of Norway
Triple: [Ingeborg of Denmark, Queen of Norway, child, Agnes Magnusdatter of Norway]
Generated description
Agnes Magnusdatter of Norway was a medieval Norwegian princess, the daughter of King Magnus VI and Queen Ingeborg of Denmark.

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_69f76dd32c008190853aef6028f60208 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f7865d9dcc81909deaf635acd9ef56 completed May 3, 2026, 5:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37cfe5223081909c89d1403f79d9d4 completed June 21, 2026, 11:49 a.m.
NEDg Description generation batch_6a37d05865e88190a303c71f529aee73 completed June 21, 2026, 11:51 a.m.
NED2 Entity disambiguation (via description) batch_6a37d0b2cdcc8190b0adaf1d50f7425c completed June 21, 2026, 11:53 a.m.
Created at: May 3, 2026, 4:01 p.m.