Triple

T3753963
Position Surface form Disambiguated ID Type / Status
Subject Orellana Province E81999 entity
Predicate capital P234 FINISHED
Object Coca
Coca is a small city in Ecuador that serves as a key gateway to the Amazon rainforest and regional oil operations.
 E385557 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: Coca | Statement: [Orellana Province, capital, Coca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Coca
Context triple: [Orellana Province, capital, Coca]
  • A. Cocaine
    Cocaine is a powerful and addictive stimulant drug derived from the coca plant, known for its euphoric effects and significant health and legal risks.
  • B. Ganja
    Ganja is one of Azerbaijan’s largest and oldest cities, known as a historic cultural and economic center in the South Caucasus.
  • C. Sabu
    Sabu is an alternative name for the Shabo language, a little-documented and possibly language-isolate tongue spoken by a small community in southwestern Ethiopia.
  • D. Cocoa
    Cocoa is Apple’s native object-oriented application framework for building graphical user interfaces and other software on macOS.
  • E. Angostura
    Angostura is the former name of Ciudad Bolívar, a historic city on the Orinoco River in southeastern Venezuela known for its colonial architecture and role in the Venezuelan War of Independence.
  • 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: Coca
Triple: [Orellana Province, capital, Coca]
Generated description
Coca is a small city in Ecuador that serves as a key gateway to the Amazon rainforest and regional oil operations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Coca
Target entity description: Coca is a small city in Ecuador that serves as a key gateway to the Amazon rainforest and regional oil operations.
  • A. Cocaine
    Cocaine is a powerful and addictive stimulant drug derived from the coca plant, known for its euphoric effects and significant health and legal risks.
  • B. Ganja
    Ganja is one of Azerbaijan’s largest and oldest cities, known as a historic cultural and economic center in the South Caucasus.
  • C. Sabu
    Sabu is an alternative name for the Shabo language, a little-documented and possibly language-isolate tongue spoken by a small community in southwestern Ethiopia.
  • D. Cocoa
    Cocoa is Apple’s native object-oriented application framework for building graphical user interfaces and other software on macOS.
  • E. Angostura
    Angostura is the former name of Ciudad Bolívar, a historic city on the Orinoco River in southeastern Venezuela known for its colonial architecture and role in the Venezuelan War of Independence.
  • 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_69ad8b1db40081908b61ffa6b78afd4d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb9340e0819083215989718b4598 completed March 8, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e50825588190b620950ac1d4f408 completed March 14, 2026, 4:33 a.m.
NEDg Description generation batch_69b4e5cd3b3c8190af6ca28a6772625c completed March 14, 2026, 4:36 a.m.
NED2 Entity disambiguation (via description) batch_69b4e671e02c819094cae2a3a2abb1b4 completed March 14, 2026, 4:39 a.m.
Created at: March 8, 2026, 3:35 p.m.