Triple

T17108864
Position Surface form Disambiguated ID Type / Status
Subject Erk Kala E415171 entity
Predicate nearbyCity P350 FINISHED
Object Mary
Mary is a city in southeastern Turkmenistan that serves as a major regional center for the country’s natural gas and cotton industries.
E80030 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: Mary | Statement: [Erk Kala, nearbyCity, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary
Context triple: [Erk Kala, nearbyCity, Mary]
  • A. Mary
    Mary is the given name of Mary Jo Kopechne, the American political campaign specialist who died in the 1969 Chappaquiddick incident involving Senator Ted Kennedy.
  • B. Mary
    Mary is the middle name of Edith Tolkien, the wife of author J.R.R. Tolkien.
  • C. Mary
    Mary is the central protagonist of the play "The Memory of Water," around whom the story’s emotional and familial conflicts revolve.
  • D. Mary
    Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
  • E. Mary
    Mary is the given name of the American stage and film actress Josephine Hull, known for her roles in classic mid-20th-century theater and cinema.
  • 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: Mary
Triple: [Erk Kala, nearbyCity, Mary]
Generated description
Mary is a city in southeastern Turkmenistan that serves as a major regional center for the country’s natural gas and cotton industries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mary
Target entity description: Mary is a city in southeastern Turkmenistan that serves as a major regional center for the country’s natural gas and cotton industries.
  • A. Mary chosen
    Mary is a significant urban and economic center in southeastern Turkmenistan, known for its role in the country’s natural gas and cotton industries.
  • B. Mary
    Mary is a feminine given name of Hebrew origin, widely used in English-speaking and many other cultures and historically associated with numerous religious and historical figures.
  • C. Mary
    Mary is a character in the "Tunnel community" setting, known as one of the individuals living within its underground society.
  • D. Mary
    Mary is a 1927 psychological novel by Vladimir Nabokov that explores memory, exile, and lost love through the reflections of a Russian émigré in Berlin.
  • E. Mary
    Mary is the given name of Mary Harriman Rumsey, an American social reformer and founder of the Junior League.
  • 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_69d886d090cc8190a39cb94992586905 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dc2906a081909d0d43cf04319f52 completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a013a03e2e48190a0b631dd8f6f8a24 completed May 11, 2026, 2:08 a.m.
NEDg Description generation batch_6a013ae388548190b09d2c81e1ab0d02 completed May 11, 2026, 2:11 a.m.
NED2 Entity disambiguation (via description) batch_6a013b4df74c81908b3b99e276531e13 completed May 11, 2026, 2:13 a.m.
Created at: April 10, 2026, 5:35 a.m.