Triple

T1328301
Position Surface form Disambiguated ID Type / Status
Subject Estadio Azteca E28380 entity
Predicate cityDistrict P2709 FINISHED
Object Santa Úrsula
Santa Úrsula is a neighborhood in Mexico City best known for hosting the iconic Estadio Azteca football stadium.
 E155258 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: Santa Úrsula | Statement: [Estadio Azteca, cityDistrict, Santa Úrsula]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Santa Úrsula
Context triple: [Estadio Azteca, cityDistrict, Santa Úrsula]
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • C. Teresa
    Teresa is the religious name of Mother Teresa, the Catholic nun and missionary renowned for her charitable work with the poor in Kolkata, India.
  • D. Aikaterine
    Aikaterine is an ancient Greek female given name that is the linguistic ancestor of various forms such as Katherine and Kathleen.
  • E. Madalena
    Madalena is a neighborhood in the Brazilian city of Recife, known for its urban character and local commerce.
  • 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: Santa Úrsula
Triple: [Estadio Azteca, cityDistrict, Santa Úrsula]
Generated description
Santa Úrsula is a neighborhood in Mexico City best known for hosting the iconic Estadio Azteca football stadium.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Santa Úrsula
Target entity description: Santa Úrsula is a neighborhood in Mexico City best known for hosting the iconic Estadio Azteca football stadium.
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • C. Teresa
    Teresa is the religious name of Mother Teresa, the Catholic nun and missionary renowned for her charitable work with the poor in Kolkata, India.
  • D. Aikaterine
    Aikaterine is an ancient Greek female given name that is the linguistic ancestor of various forms such as Katherine and Kathleen.
  • E. Madalena
    Madalena is a neighborhood in the Brazilian city of Recife, known for its urban character and local commerce.
  • 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_69a498540a2481909e807a762280d3ba completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c1c1d8188190b15a641a08345adc completed March 1, 2026, 10:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69acce641c6481908a3d2b9e9fc423d7 completed March 8, 2026, 1:18 a.m.
NEDg Description generation batch_69accecd233c8190bebf5395e8dd5961 completed March 8, 2026, 1:20 a.m.
NED2 Entity disambiguation (via description) batch_69accf2c65188190844bcd1c5efa563a completed March 8, 2026, 1:21 a.m.
Created at: March 1, 2026, 7:55 p.m.