Triple

T14554530
Position Surface form Disambiguated ID Type / Status
Subject Iraq-i Ajam E341504 entity
Predicate hasMajorCity P316 FINISHED
Object Ray
Ray is an ancient city in northern Iran, historically significant as a major urban center on the Silk Road and a predecessor to modern Tehran.
E255763 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: [Iraq-i Ajam, hasMajorCity, Ray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ray
Context triple: [Iraq-i Ajam, hasMajorCity, Ray]
  • A. 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.
  • 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 the protagonist of the novel "The Keep," around whom the story’s central psychological and narrative tensions revolve.
  • D. Ray
    Ray is the optimistic Cajun firefly from Disney’s *The Princess and the Frog*, known for his devotion to his love “Evangeline” and his role in aiding Tiana and Naveen.
  • E. Ray
    Ray is the romantic, Cajun firefly character from Disney’s animated film "The Princess and the Frog," known for his heartfelt song "Ma Belle Evangeline."
  • 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: [Iraq-i Ajam, hasMajorCity, Ray]
Generated description
Ray is an ancient city in northern Iran, historically significant as a major urban center on the Silk Road and a predecessor to modern Tehran.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Ray
Target entity description: Ray is an ancient city in northern Iran, historically significant as a major urban center on the Silk Road and a predecessor to modern Tehran.
  • A. Ray chosen
    Ray is an ancient city near modern-day Tehran in Iran that served as a significant political and cultural center in various Persian empires.
  • B. Ray
    Ray is a masculine given name commonly used in English-speaking countries, often as a short form of Raymond.
  • C. Ray
    Ray is a surname of English and Scottish origin borne by various notable individuals across different fields.
  • D. 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.
  • 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.

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_69d822db9c8481908213ceb39585f792 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb2f00cec8190a7b6482d18b9a216 completed April 14, 2026, 9:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd8ab24f8c8190bb0e68ebb854844d completed May 8, 2026, 7:03 a.m.
NEDg Description generation batch_69fd8b686ef081908b3f3ddedde12685 completed May 8, 2026, 7:06 a.m.
NED2 Entity disambiguation (via description) batch_69fd8c9920108190ae4eea3e1d990ea2 completed May 8, 2026, 7:11 a.m.
Created at: April 10, 2026, 1:23 a.m.