Triple

T13108818
Position Surface form Disambiguated ID Type / Status
Subject In Bruges E310917 entity
Predicate hasCharacter P2308 FINISHED
Object Ray
Ray is the guilt-ridden hitman protagonist of the dark comedy crime film "In Bruges," portrayed by Colin Farrell.
 E1021460 NE FINISHED

How this triple was built (4 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: Ray | Statement: [In Bruges, hasCharacter, Ray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ray
Context triple: [In Bruges, hasCharacter, Ray]
  • A. Ray
    "Ray" is a 2004 biographical film about the life and music of legendary rhythm and blues musician Ray Charles.
  • B. Ray
    Ray is an ancient city near modern-day Tehran in Iran that served as a significant political and cultural center in various Persian empires.
  • C. Ray
    Ray is an open-source distributed computing framework designed to scale Python applications for tasks like machine learning, reinforcement learning, and data processing across clusters.
  • D. Ray
    Ray is a surname of English and Scottish origin borne by various notable individuals across different fields.
  • E. Ray
    Ray is the central figure in Claude McKay’s novel "Home to Harlem," embodying the intellectual, conflicted perspective on Black identity and urban life during the Harlem Renaissance.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ray
Triple: [In Bruges, hasCharacter, Ray]
Generated description
Ray is the guilt-ridden hitman protagonist of the dark comedy crime film "In Bruges," portrayed by Colin Farrell.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ray
Target entity description: Ray is the guilt-ridden hitman protagonist of the dark comedy crime film "In Bruges," portrayed by Colin Farrell.
  • A. Ray
    "Ray" is a 2004 biographical film about the life and music of legendary rhythm and blues musician Ray Charles.
  • B. Ray
    Ray is the central figure in Claude McKay’s novel "Home to Harlem," embodying the intellectual, conflicted perspective on Black identity and urban life during the Harlem Renaissance.
  • C. Ray
    Ray is a masculine given name commonly used in English-speaking countries, often as a short form of Raymond.
  • D. Ray
    Ray is a surname of English and Scottish origin borne by various notable individuals across different fields.
  • E. Ray
    Ray is an open-source distributed computing framework designed to scale Python applications for tasks like machine learning, reinforcement learning, and data processing across clusters.
  • 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_69d806a872d08190a329806f8ff30df4 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9817ce07881909ec552bf861ac175 completed April 10, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e27bd8fc8190a81130b7cb3a8bd8 completed May 3, 2026, 5:51 a.m.
NEDg Description generation batch_69f6e37023d48190ba6a0b790ed23370 completed May 3, 2026, 5:56 a.m.
NED2 Entity disambiguation (via description) batch_69f6e407dd988190b928b8931985a815 completed May 3, 2026, 5:58 a.m.
Created at: April 9, 2026, 9:05 p.m.