Triple

T15136680
Position Surface form Disambiguated ID Type / Status
Subject Persian Iraq E361572 entity
Predicate includesCity P3207 FINISHED
Object Ray
Ray is an ancient city in north-central 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: [Persian Iraq, includesCity, Ray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ray
Context triple: [Persian Iraq, includesCity, Ray]
  • A. Ray
    Ray is a masculine given name commonly used in English-speaking countries, often as a short form of Raymond.
  • B. Ray
    Ray is the protagonist of the novel "The Keep," around whom the story’s central psychological and narrative tensions revolve.
  • C. 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.
  • D. 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."
  • 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
  • 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: [Persian Iraq, includesCity, Ray]
Generated description
Ray is an ancient city in north-central 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 north-central 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 the middle name of Lola Ray Facinelli, a member of the Facinelli family.
  • 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 a surname of English and Scottish origin borne by various notable individuals across different fields.
  • 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_69d85a06450081909c5a14ea9851a15e completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e005b3f6f48190b1ed7c7b28feb7a6 completed April 15, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69febfea8e3081909551a8e3936c13a6 completed May 9, 2026, 5:02 a.m.
NEDg Description generation batch_69fec2f5b7bc8190a0038b34a958d692 completed May 9, 2026, 5:15 a.m.
NED2 Entity disambiguation (via description) batch_69fec38062a08190b141c311b42baaf5 completed May 9, 2026, 5:17 a.m.
Created at: April 10, 2026, 3:07 a.m.