Triple

T38550566
Position Surface form Disambiguated ID Type / Status
Subject Karen David E925097 entity
Predicate notableRole P22 FINISHED
Object Princess Isabella in Galavant
Princess Isabella in Galavant is a brave, quick-witted princess and main heroine in the musical comedy TV series "Galavant," known for her sharp humor and adventurous spirit.
E2274221 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: Princess Isabella in Galavant | Statement: [Karen David, notableRole, Princess Isabella in Galavant]
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: Princess Isabella in Galavant
Triple: [Karen David, notableRole, Princess Isabella in Galavant]
Generated description
Princess Isabella in Galavant is a brave, quick-witted princess and main heroine in the musical comedy TV series "Galavant," known for her sharp humor and adventurous spirit.

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_69f76eaeb69c8190b367df9330d6f6af completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fcd3163d0881909d3209cd7cb81c10 completed May 7, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41e03bcfe88190a85e65bfab80301d completed June 29, 2026, 3:02 a.m.
NEDg Description generation batch_6a41e15c67ac8190b877a4bfd4e7499c completed June 29, 2026, 3:07 a.m.
NED2 Entity disambiguation (via description) batch_6a41e1d6985081909748d210df210c84 completed June 29, 2026, 3:09 a.m.
Created at: May 3, 2026, 4:32 p.m.