Triple

T35820387
Position Surface form Disambiguated ID Type / Status
Subject Lieutenant Destin Mattias E1035481 entity
Predicate loyalTo P1201 FINISHED
Object King Runeard
King Runeard is a former ruler of Arendelle in Disney's Frozen franchise, known for his secretive and antagonistic actions that sparked conflict with the Northuldra people.
E2156540 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: King Runeard | Statement: [Lieutenant Destin Mattias, loyalTo, King Runeard]
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: King Runeard
Triple: [Lieutenant Destin Mattias, loyalTo, King Runeard]
Generated description
King Runeard is a former ruler of Arendelle in Disney's Frozen franchise, known for his secretive and antagonistic actions that sparked conflict with the Northuldra people.

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_69f76e185ffc8190880b3cdf51decd38 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a8fe213881908773a4990299aabe completed May 3, 2026, 7:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38917ae93c8190b165d2d2684ad28f completed June 22, 2026, 1:35 a.m.
NEDg Description generation batch_6a389253d4f881909a40e2c14b4a6d4e completed June 22, 2026, 1:39 a.m.
NED2 Entity disambiguation (via description) batch_6a38930372408190a387347aba837518 completed June 22, 2026, 1:42 a.m.
Created at: May 3, 2026, 4:06 p.m.