Triple

T16670271
Position Surface form Disambiguated ID Type / Status
Subject Melissa Reese E405089 entity
Predicate givenName P17 FINISHED
Object Melissa E264271 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: Melissa | Statement: [Melissa Reese, givenName, Melissa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Melissa
Context triple: [Melissa Reese, givenName, Melissa]
  • A. Melissa
    Melissa is a small but rapidly growing suburban city in North Texas, located within the Dallas–Fort Worth metropolitan area.
  • B. Melissa chosen
    Melissa is a feminine given name commonly used in English-speaking countries, derived from the Greek word for "honeybee."
  • C. Melissa
    "Melissa" is a classic, melodic Southern rock ballad by the Allman Brothers Band, known for its gentle acoustic sound and reflective lyrics.
  • D. Melva
    Melva is a character in Richard Bruce Nugent’s modernist short story "Smoke, Lilies and Jade," which explores themes of race, sexuality, and artistic identity during the Harlem Renaissance.
  • E. Melinda
    Melinda is a young, impressionable girl in the play "Inherit the Wind," serving as a minor character who reflects the town’s attitudes during the famous trial.
  • 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_69d8838b5fbc81908c6575c132b82e80 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37ca079ec819090b356c86a9241cc completed April 18, 2026, 12:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a008a3692588190a94d349cb63d9749 completed May 10, 2026, 1:37 p.m.
Created at: April 10, 2026, 5:18 a.m.