Triple

T9594784
Position Surface form Disambiguated ID Type / Status
Subject Campo de Mayo E231502 entity
Predicate nearbyCity P350 FINISHED
Object San Miguel
San Miguel is a city in the Greater Buenos Aires metropolitan area of Argentina, located in the northwest of Buenos Aires Province.
 E808549 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: San Miguel | Statement: [Campo de Mayo, nearbyCity, San Miguel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Miguel
Context triple: [Campo de Mayo, nearbyCity, San Miguel]
  • A. San Miguel
    San Miguel is an active stratovolcano in eastern El Salvador, known for its frequent eruptions and prominent conical shape within the Central American volcanic chain.
  • B. San Miguel
    San Miguel is a town located within Bolívar Province in central Ecuador, known for its Andean setting and local agricultural activities.
  • C. San Miguel
    San Miguel is a landlocked agricultural municipality in the province of Bulacan in the Philippines, known for its historical sites and rural communities.
  • D. San Miguel
    San Miguel is a municipality located in Colombia’s southern Putumayo Department, near the border with Ecuador.
  • E. San Miguel
    San Miguel is a coastal district of Lima, Peru, known for its residential areas, shopping centers, and access to major avenues and the Pacific shoreline.
  • 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: San Miguel
Triple: [Campo de Mayo, nearbyCity, San Miguel]
Generated description
San Miguel is a city in the Greater Buenos Aires metropolitan area of Argentina, located in the northwest of Buenos Aires Province.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: San Miguel
Target entity description: San Miguel is a city in the Greater Buenos Aires metropolitan area of Argentina, located in the northwest of Buenos Aires Province.
  • A. San Miguel
    San Miguel is a municipality located in Colombia’s southern Putumayo Department, near the border with Ecuador.
  • B. San Miguel
    San Miguel is a coastal district of Lima, Peru, known for its residential areas, shopping centers, and access to major avenues and the Pacific shoreline.
  • C. San Miguel
    San Miguel is a barangay (local administrative district) within the highly urbanized city of Taguig in Metro Manila, Philippines.
  • D. San Miguel
    San Miguel is a town located within Bolívar Province in central Ecuador, known for its Andean setting and local agricultural activities.
  • E. San Miguel
    San Miguel is a landlocked agricultural municipality in the province of Bulacan in the Philippines, known for its historical sites and rural communities.
  • 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_69ca8482884481908eccdfdf64d6fbf7 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9a14fd308190beb8fda5a9e912c7 completed April 1, 2026, 10:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69d16199845881908f8a91182ca48250 completed April 4, 2026, 7:08 p.m.
NEDg Description generation batch_69d16230a99481909d82d03babe6729a completed April 4, 2026, 7:10 p.m.
NED2 Entity disambiguation (via description) batch_69d16321aba88190a7e8359dbaa5362b completed April 4, 2026, 7:14 p.m.
Created at: March 30, 2026, 8:07 p.m.