Triple

T15193804
Position Surface form Disambiguated ID Type / Status
Subject Chinatown – score E363092 entity
Predicate themeFunction P25955 FINISHED
Object underscores tragedy and corruption in Chinatown (film) LITERAL 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: underscores tragedy and corruption in Chinatown (film) | Statement: [Chinatown – score, themeFunction, underscores tragedy and corruption in Chinatown (film)]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: themeFunction
Context triple: [Chinatown – score, themeFunction, underscores tragedy and corruption in Chinatown (film)]
  • A. thematicFunction
    Indicates how an entity participates in or contributes to the role structure of an event or situation (e.g., as agent, patient, instrument, etc.).
  • B. themeFor chosen
    Indicates that something serves as the central subject, topic, or focus for another thing (such as an event, work, or activity).
  • C. themeKey
    Indicates that one entity serves as the primary subject, topic, or thematic focus associated with another entity.
  • D. themeChange
    Indicates that an entity undergoes a change in its theme, style, or subject, typically transitioning from one thematic state or configuration to another.
  • E. theme
    Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
  • F. None of above.

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_69d85a09a39c81908759f23268e2d408 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0067eb710819085211fd05d5fa5f0 completed April 15, 2026, 9:43 p.m.
PD Predicate disambiguation batch_69deb97bd8bc8190b2ad4888f97cf963 completed April 14, 2026, 10:02 p.m.
Created at: April 10, 2026, 3:10 a.m.