Triple

T4452282
Position Surface form Disambiguated ID Type / Status
Subject Frank Money E97640 entity
Predicate leaves P56600 FINISHED
Object Lily E442534 NE FINISHED

How this triple was built (3 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: Lily | Statement: [Frank Money, leaves, Lily]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lily
Context triple: [Frank Money, leaves, Lily]
  • A. Lily
    Lily is a feminine given name of English origin commonly associated with the lily flower and symbolizing purity and beauty.
  • B. Lily
    Lily is a pivotal character in the psychological thriller film "Black Swan," serving as a seductive and enigmatic rival whose presence intensifies the protagonist's descent into paranoia and self-destruction.
  • C. Lily chosen
    Lily is a woman romantically involved with Frank Money in Toni Morrison’s novel "Home."
  • D. Lilly
    Lilly is the surname of Bob Lilly, a Pro Football Hall of Fame defensive tackle best known for his career with the Dallas Cowboys.
  • E. Lily Bell
    Lily Bell is a central character in the television series "Hell on Wheels," portrayed as a determined and resourceful Englishwoman navigating the dangers and politics surrounding the construction of the transcontinental railroad.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: leaves
Context triple: [Frank Money, leaves, Lily]
  • A. leafType
    Indicates the specific kind or classification of leaf associated with an entity.
  • B. leafColor
    Indicates the color or coloration characteristics of a leaf in relation to a plant or plant part.
  • C. boLeavesRepresent
    Indicates that one entity’s leaves serve as a symbolic or visual representation of another entity.
  • D. typicalLeafCharacteristic
    Indicates the usual or defining features of a leaf that characterize it under normal conditions.
  • E. hasLeaves
    Indicates that an entity possesses leaves as part of its structure or form.
  • F. None of above. chosen

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_69b3454777808190b78aa9047ba1f018 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b355f3731c81909cc5a782b12ddd38 completed March 13, 2026, 12:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6376286e48190906874d67c730dc9 completed March 15, 2026, 4:36 a.m.
PD Predicate disambiguation batch_69b34f649df081909d3cc2f6a1b8f282 completed March 12, 2026, 11:42 p.m.
PDg Predicate description generation batch_69b354e1f0948190b645096b2b7037af completed March 13, 2026, 12:05 a.m.
Created at: March 12, 2026, 11:33 p.m.