Triple

T6162266
Position Surface form Disambiguated ID Type / Status
Subject Mia Thermopolis E137468 entity
Predicate friend P8712 FINISHED
Object Michael Moscovitz E574374 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: Michael Moscovitz | Statement: [Mia Thermopolis, friend, Michael Moscovitz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Moscovitz
Context triple: [Mia Thermopolis, friend, Michael Moscovitz]
  • A. Michael Moscovitz chosen
    Michael Moscovitz is a central character in Meg Cabot’s "The Princess Diaries" series, known as Mia Thermopolis’s witty, musically inclined romantic interest and eventual boyfriend.
  • B. Michael Shvo
    Michael Shvo is a high-profile real estate developer and art collector known for leading luxury property projects in major global cities.
  • C. Michael Greenburg
    Michael Greenburg is an American film and television producer best known for his work on projects such as the series "Stargate SG-1" and for his former marriage to actress Sharon Stone.
  • D. Michael Greenberg
    Michael Greenberg is a prominent American neuroscientist renowned for his pioneering work on activity-dependent gene expression in the brain.
  • E. Michael Markowitz
    Michael Markowitz is an American comedy writer best known for co-writing the hit film "Horrible Bosses."
  • 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_69c008a54fc88190b6ce4416490ca79d completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05d371484819090c18b62b095b49e completed March 22, 2026, 9:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c16ee26ed8819084a5cfd84ba9564e completed March 23, 2026, 4:48 p.m.
Created at: March 22, 2026, 4:17 p.m.