Triple

T15909530
Position Surface form Disambiguated ID Type / Status
Subject Regina George E385808 entity
Predicate hasFamilyMember P7844 FINISHED
Object Kylie George (younger sister) E1183717 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: Kylie George (younger sister) | Statement: [Regina George, hasFamilyMember, Kylie George (younger sister)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kylie George (younger sister)
Context triple: [Regina George, hasFamilyMember, Kylie George (younger sister)]
  • A. Kylie George chosen
    Kylie George is a lesser-known sibling of the fictional character Regina George from the film "Mean Girls."
  • B. Kylie Rogers
    Kylie Rogers is an American actress best known for her lead role as the young girl in the faith-based drama film "Miracles from Heaven."
  • C. Kylie Dodson
    Kylie Dodson is an individual associated with the use of something referred to as "Dodson," though little public information is available about her beyond this connection.
  • D. Kylie Gillies
    Kylie Gillies is an Australian television presenter best known as the longtime co-host of the Seven Network’s morning program The Morning Show.
  • E. Kylie Peppler
    Kylie Peppler is a learning sciences researcher known for her work on creative computing, maker education, and the integration of arts and technology in STEM learning.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d86da686e4819097cbf3b1fc2d881d completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1565ea7a8819097efffda366b5245 completed April 16, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb5a5b0dc81909606d667c3bc0edf completed May 9, 2026, 10:31 p.m.
Created at: April 10, 2026, 4:52 a.m.