Triple

T3540319
Position Surface form Disambiguated ID Type / Status
Subject Order of Logohu E74867 entity
Predicate hasPostNominalLetters P1804 FINISHED
Object OL
OL is the post-nominal abbreviation used by recipients of Papua New Guinea’s Order of Logohu, a national honor recognizing distinguished service.
 E366824 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: OL | Statement: [Order of Logohu, hasPostNominalLetters, OL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: OL
Context triple: [Order of Logohu, hasPostNominalLetters, OL]
  • A. OL
    OL is a UK postcode area covering Oldham and surrounding parts of Greater Manchester and nearby regions in North West England.
  • B. OL
    OL is the commonly used abbreviation for Olympique Lyonnais, a major French football club best known internationally for its highly successful women's team.
  • C. OL
    OL is the vehicle registration code for the city of Oldenburg in the German state of Lower Saxony.
  • D. OLE
    OLE (Object Linking and Embedding) is a Microsoft technology that enables embedding and linking to documents and other objects within different applications, forming a foundation for later component technologies like ActiveX.
  • E. OLA
    OLA is the commonly used acronym for the United Nations Office of Legal Affairs, which provides legal advice and support to UN organs and specialized agencies.
  • 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: OL
Triple: [Order of Logohu, hasPostNominalLetters, OL]
Generated description
OL is the post-nominal abbreviation used by recipients of Papua New Guinea’s Order of Logohu, a national honor recognizing distinguished service.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: OL
Target entity description: OL is the post-nominal abbreviation used by recipients of Papua New Guinea’s Order of Logohu, a national honor recognizing distinguished service.
  • A. OL
    OL is a UK postcode area covering Oldham and surrounding parts of Greater Manchester and nearby regions in North West England.
  • B. OL
    OL is the commonly used abbreviation for Olympique Lyonnais, a major French football club best known internationally for its highly successful women's team.
  • C. OL
    OL is the vehicle registration code for the city of Oldenburg in the German state of Lower Saxony.
  • D. OLE
    OLE (Object Linking and Embedding) is a Microsoft technology that enables embedding and linking to documents and other objects within different applications, forming a foundation for later component technologies like ActiveX.
  • E. OLA
    OLA is the commonly used acronym for the United Nations Office of Legal Affairs, which provides legal advice and support to UN organs and specialized agencies.
  • 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_69ad85d274cc8190ab59c97298a1cfbf completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbccbbb5c8190a951754dda5fc642 completed March 8, 2026, 6:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69b38bd7fa3881909fee11cc6f4af7ea completed March 13, 2026, 4 a.m.
NEDg Description generation batch_69b38c9259f881908068961f4eaf11a0 completed March 13, 2026, 4:03 a.m.
NED2 Entity disambiguation (via description) batch_69b38d04eb388190af64f990bac8d140 completed March 13, 2026, 4:05 a.m.
Created at: March 8, 2026, 3:20 p.m.