Triple

T15072114
Position Surface form Disambiguated ID Type / Status
Subject The Drop E379902 entity
Predicate follows P134 FINISHED
Object Nine Dragons E379901 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: Nine Dragons | Statement: [The Drop, follows, Nine Dragons]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nine Dragons
Context triple: [The Drop, follows, Nine Dragons]
  • A. Nine Dragons chosen
    "Nine Dragons" is a crime novel by Michael Connelly featuring LAPD detective Harry Bosch as he investigates a case that takes him from Los Angeles to Hong Kong.
  • B. Crouching Dragon
    Crouching Dragon is the famous style name of Zhuge Liang, the legendary strategist and statesman of the Three Kingdoms period in Chinese history.
  • C. The Chinese Dragon
    "The Chinese Dragon" is a poem that draws on traditional Chinese dragon imagery and symbolism, likely exploring themes of myth, power, and cultural identity.
  • D. Golden Dragon
    The Golden Dragon is a prestigious award presented at the Kraków Film Festival, recognizing outstanding achievements in short and documentary filmmaking.
  • E. Four Dragon Kings
    The Four Dragon Kings are powerful mythological deities in East Asian tradition who each rule over one of the four seas and control rain and water.
  • 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_69d85cd7683881908d405c1b5d7b4f7f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dff7fa0570819088a97b28173154cd completed April 15, 2026, 8:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69feae11d6648190bc9b5d4f520d694b completed May 9, 2026, 3:46 a.m.
Created at: April 10, 2026, 3:02 a.m.