Triple

T13490469
Position Surface form Disambiguated ID Type / Status
Subject Machine Readable Travel Documents E318619 entity
Predicate hasComponent P35 FINISHED
Object MRZ
MRZ (Machine Readable Zone) is the standardized, optically scannable text area on passports and other travel documents that encodes key personal and document data for automated identity verification.
 E1043719 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: MRZ | Statement: [Machine Readable Travel Documents, hasComponent, MRZ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MRZ
Context triple: [Machine Readable Travel Documents, hasComponent, MRZ]
  • A. MZ
    MZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Mozambique.
  • B. MZ
    MZ is the vehicle registration code for the Mainz region in Germany, which includes Ingelheim am Rhein.
  • C. MRK
    MRK is the stock ticker symbol for Merck & Co., a major global pharmaceutical company known for developing prescription medicines, vaccines, and animal health products.
  • D. MRK
    MRK is the station code for Merrick, a Long Island Rail Road commuter rail station in Merrick, New York.
  • E. MSZ
    MSZ is the commonly used abbreviation for Poland's Ministry of Foreign Affairs, the government body responsible for the country's foreign policy and international relations.
  • 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: MRZ
Triple: [Machine Readable Travel Documents, hasComponent, MRZ]
Generated description
MRZ (Machine Readable Zone) is the standardized, optically scannable text area on passports and other travel documents that encodes key personal and document data for automated identity verification.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MRZ
Target entity description: MRZ (Machine Readable Zone) is the standardized, optically scannable text area on passports and other travel documents that encodes key personal and document data for automated identity verification.
  • A. MZ
    MZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Mozambique.
  • B. MZ
    MZ is the vehicle registration code for the Mainz region in Germany, which includes Ingelheim am Rhein.
  • C. MRK
    MRK is the stock ticker symbol for Merck & Co., a major global pharmaceutical company known for developing prescription medicines, vaccines, and animal health products.
  • D. MRK
    MRK is the station code for Merrick, a Long Island Rail Road commuter rail station in Merrick, New York.
  • E. MSZ
    MSZ is the commonly used abbreviation for Poland's Ministry of Foreign Affairs, the government body responsible for the country's foreign policy and international relations.
  • 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_69d806b6bfec819089222715b2e86c8e completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf3cbe2081908c6792362c67c8f1 completed April 12, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7463b758c8190abc0dd2a049d751e completed May 3, 2026, 12:57 p.m.
NEDg Description generation batch_69f74d048250819098baf78ff08c1633 completed May 3, 2026, 1:26 p.m.
NED2 Entity disambiguation (via description) batch_69f750f7f11481908c60b49eef65b63f completed May 3, 2026, 1:43 p.m.
Created at: April 9, 2026, 9:43 p.m.