Triple

T11123197
Position Surface form Disambiguated ID Type / Status
Subject Macas E263067 entity
Predicate hasRegionCode P3446 FINISHED
Object EC-M
EC-M is the vehicle registration and regional code assigned to the Macas area in Ecuador.
 E906149 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: EC-M | Statement: [Macas, hasRegionCode, EC-M]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: EC-M
Context triple: [Macas, hasRegionCode, EC-M]
  • A. EMCC
    EMCC is a public community college in Bangor, Maine, offering two-year degree and certificate programs focused on career and technical education.
  • B. ECM
    ECM is a financial forecasting model developed by economist Martin Armstrong that predicts economic cycles and market turning points based on a recurring time interval.
  • C. ECUM
    ECUM is the School of Sciences at the University of Minho, a Portuguese higher education institution focused on scientific education and research.
  • D. EKM
    EKM is a Protestant regional church body in Germany that forms part of the Evangelical Church in Germany (EKD).
  • E. EC4
    EC4 is a central London postcode district covering parts of the City of London, including key financial and commercial areas around Cannon Street and St Paul’s.
  • 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: EC-M
Triple: [Macas, hasRegionCode, EC-M]
Generated description
EC-M is the vehicle registration and regional code assigned to the Macas area in Ecuador.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: EC-M
Target entity description: EC-M is the vehicle registration and regional code assigned to the Macas area in Ecuador.
  • A. EMCC
    EMCC is a public community college in Bangor, Maine, offering two-year degree and certificate programs focused on career and technical education.
  • B. ECM
    ECM is a financial forecasting model developed by economist Martin Armstrong that predicts economic cycles and market turning points based on a recurring time interval.
  • C. ECUM
    ECUM is the School of Sciences at the University of Minho, a Portuguese higher education institution focused on scientific education and research.
  • D. EKM
    EKM is a Protestant regional church body in Germany that forms part of the Evangelical Church in Germany (EKD).
  • E. EC4
    EC4 is a central London postcode district covering parts of the City of London, including key financial and commercial areas around Cannon Street and St Paul’s.
  • 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_69d6aa9b46cc8190b19f9f0cc45bf322 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7e82e933481908550499cf9dd6531 completed April 9, 2026, 5:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69e42d8643748190a7801fc401dd2b5a completed April 19, 2026, 1:19 a.m.
NEDg Description generation batch_69e42f3eaa0c819095e5af20b910d979 completed April 19, 2026, 1:26 a.m.
NED2 Entity disambiguation (via description) batch_69e43771eaec8190be9bb709723931e0 completed April 19, 2026, 2:01 a.m.
Created at: April 8, 2026, 9:28 p.m.