Triple

T6093602
Position Surface form Disambiguated ID Type / Status
Subject Bolívar Department E135824 entity
Predicate contains P35 FINISHED
Object María La Baja
María La Baja is a municipality and town in northern Colombia known for its agricultural economy and location within the Caribbean region.
E567965 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: María La Baja | Statement: [Bolívar Department, contains, María La Baja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: María La Baja
Context triple: [Bolívar Department, contains, María La Baja]
  • A. Santa Rosalía
    Santa Rosalía is a historic mining town and port on the eastern coast of the Baja California Peninsula in Mexico, known for its French-influenced architecture and copper mining heritage.
  • 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. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • D. María
    María is the given first name of Josefa Ortiz de Domínguez, a prominent figure in Mexico’s War of Independence.
  • E. María
    María is a feminine given name of Hebrew origin, widely used in Spanish-speaking countries and associated with numerous historical and religious figures.
  • 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: María La Baja
Triple: [Bolívar Department, contains, María La Baja]
Generated description
María La Baja is a municipality and town in northern Colombia known for its agricultural economy and location within the Caribbean region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: María La Baja
Target entity description: María La Baja is a municipality and town in northern Colombia known for its agricultural economy and location within the Caribbean region.
  • A. Santa Rosalía
    Santa Rosalía is a historic mining town and port on the eastern coast of the Baja California Peninsula in Mexico, known for its French-influenced architecture and copper mining heritage.
  • 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. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • D. María
    María is a feminine given name of Hebrew origin, widely used in Spanish-speaking countries and associated with numerous historical and religious figures.
  • E. María
    "María" is a 1995 Latin pop hit by Puerto Rican singer Ricky Martin that became one of his signature international breakthrough songs.
  • 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_69c0087cd3c48190b459848c72d84eb1 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c05a9516ec819093e94ee8d3244e1b completed March 22, 2026, 9:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c125365a7481909e40d01c2d3590aa completed March 23, 2026, 11:34 a.m.
NEDg Description generation batch_69c125c1a230819095dd0a56309880eb completed March 23, 2026, 11:36 a.m.
NED2 Entity disambiguation (via description) batch_69c126c641448190a826c213e8ab05af completed March 23, 2026, 11:40 a.m.
Created at: March 22, 2026, 4:12 p.m.