Triple

T3003880
Position Surface form Disambiguated ID Type / Status
Subject Mount Longdon E81850 entity
Predicate associatedWith P37 FINISHED
Object 3 PARA
3 PARA is a battalion of the British Army’s Parachute Regiment renowned for its airborne infantry role and distinguished combat service, including in the Falklands War.
 E319438 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: 3 PARA | Statement: [Mount Longdon, associatedWith, 3 PARA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 3 PARA
Context triple: [Mount Longdon, associatedWith, 3 PARA]
  • A. PAR
    PAR is the IATA city code representing the collective airport system serving Paris, France, including major airports such as Charles de Gaulle and Orly.
  • B. Parap
    Parap is an inner-city suburb of Darwin in Australia's Northern Territory, known for its popular weekend markets and tropical, laid-back atmosphere.
  • C. POR
    POR is the three-letter FIFA country code used to represent the Portugal national football team in international competitions and rankings.
  • D. T3
    T3 is one of the tram lines of the Trambaix light rail network serving the Barcelona metropolitan area.
  • E. SA3
    SA3 is the 3GPP security working group responsible for specifying and evolving security architecture and mechanisms across mobile communication standards.
  • 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: 3 PARA
Triple: [Mount Longdon, associatedWith, 3 PARA]
Generated description
3 PARA is a battalion of the British Army’s Parachute Regiment renowned for its airborne infantry role and distinguished combat service, including in the Falklands War.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 3 PARA
Target entity description: 3 PARA is a battalion of the British Army’s Parachute Regiment renowned for its airborne infantry role and distinguished combat service, including in the Falklands War.
  • A. PAR
    PAR is the IATA city code representing the collective airport system serving Paris, France, including major airports such as Charles de Gaulle and Orly.
  • B. Parap
    Parap is an inner-city suburb of Darwin in Australia's Northern Territory, known for its popular weekend markets and tropical, laid-back atmosphere.
  • C. POR
    POR is the three-letter FIFA country code used to represent the Portugal national football team in international competitions and rankings.
  • D. T3
    T3 is one of the tram lines of the Trambaix light rail network serving the Barcelona metropolitan area.
  • E. SA3
    SA3 is the 3GPP security working group responsible for specifying and evolving security architecture and mechanisms across mobile communication standards.
  • 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_69ad8b1c4de88190a83b7cefaa1f2842 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9a149b248190ac4f11afc4871cc1 completed March 8, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b12e5302c881908294827106b314e4 completed March 11, 2026, 8:56 a.m.
NEDg Description generation batch_69b12ed522148190b25ad1de42b1604d completed March 11, 2026, 8:59 a.m.
NED2 Entity disambiguation (via description) batch_69b1d65c9550819081e8734cece6ff13 completed March 11, 2026, 8:53 p.m.
Created at: March 8, 2026, 2:59 p.m.