Triple

T37890251
Position Surface form Disambiguated ID Type / Status
Subject Gustav Horn E945111 entity
Predicate spouse P13 FINISHED
Object Kristina Oxenstierna
Kristina Oxenstierna was a Swedish noblewoman from the influential Oxenstierna family, known primarily as the wife of the prominent military commander and statesman Gustav Horn.
E2248364 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: Kristina Oxenstierna | Statement: [Gustav Horn, spouse, Kristina Oxenstierna]
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: Kristina Oxenstierna
Triple: [Gustav Horn, spouse, Kristina Oxenstierna]
Generated description
Kristina Oxenstierna was a Swedish noblewoman from the influential Oxenstierna family, known primarily as the wife of the prominent military commander and statesman Gustav Horn.

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_69f76ef02668819089e7940c4001af5e completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbbd24eb088190b10e7eb41e4493a9 completed May 6, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a410cc0dfa08190b6415b202d4efb94 completed June 28, 2026, noon
NEDg Description generation batch_6a410d70ba0c8190bdcab9e762c92884 completed June 28, 2026, 12:02 p.m.
NED2 Entity disambiguation (via description) batch_6a410e3dd828819099fc3a413bcfbeb9 completed June 28, 2026, 12:06 p.m.
Created at: May 3, 2026, 4:19 p.m.