Triple

T16072483
Position Surface form Disambiguated ID Type / Status
Subject Mark Loring E389896 entity
Predicate conflictWith P4897 FINISHED
Object Vanessa Loring E387081 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: Vanessa Loring | Statement: [Mark Loring, conflictWith, Vanessa Loring]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vanessa Loring
Context triple: [Mark Loring, conflictWith, Vanessa Loring]
  • A. Vanessa Loring chosen
    Vanessa Loring is a key supporting character in the film "Juno," portrayed as a woman longing to adopt a child and struggling with the complexities of marriage and motherhood.
  • B. Vanessa Applegate
    Vanessa Applegate is a science fiction fanzine editor known for her work on the Hugo Award–winning publication Journey Planet.
  • C. Vanessa Lampley
    Vanessa Lampley is a character in Stephen King and Owen King's novel "Sleeping Beauties," involved in the unfolding crisis when women around the world fall into a mysterious sleep.
  • D. Vanessa Woods
    Vanessa Woods is an Australian science writer and researcher known for her work on primate cognition and her popular science books about dogs, bonobos, and human evolution.
  • E. Vanessa Ferlito
    Vanessa Ferlito is an American actress known for her roles in films like "Death Proof" and TV series such as "CSI: NY" and "NCIS: New Orleans."
  • 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_69d86daf32ec8190a8c0466c8f49c3c0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e183bf6c488190b0099a00f13f2a69 completed April 17, 2026, 12:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a006796bed4819085d988d7f2d7afcb completed May 10, 2026, 11:10 a.m.
Created at: April 10, 2026, 4:57 a.m.