Triple

T9857184
Position Surface form Disambiguated ID Type / Status
Subject John Paul II Catholic University of Lublin E239616 entity
Predicate shortName P43 FINISHED
Object KUL
KUL is a prominent Catholic university in Lublin, Poland, known for its strong tradition in the humanities, social sciences, and theology.
 E825691 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: KUL | Statement: [John Paul II Catholic University of Lublin, shortName, KUL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KUL
Context triple: [John Paul II Catholic University of Lublin, shortName, KUL]
  • A. KUL
    KUL is the IATA airport code for Kuala Lumpur International Airport, the main international gateway serving Malaysia’s capital region.
  • B. KAL
    KAL is the ICAO airline designator used to identify Korean Air in international aviation operations.
  • C. KLu
    KLu is the commonly used abbreviation for the Royal Netherlands Air Force, the aerial warfare branch of the Dutch armed forces.
  • D. KLE
    KLE is the vehicle registration code for the district of Cleves (Kleve) in the German state of North Rhine-Westphalia.
  • E. KLS
    KLS is a research center at Kiel University focused on interdisciplinary life science studies, including molecular biology, medicine, and environmental sciences.
  • 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: KUL
Triple: [John Paul II Catholic University of Lublin, shortName, KUL]
Generated description
KUL is a prominent Catholic university in Lublin, Poland, known for its strong tradition in the humanities, social sciences, and theology.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KUL
Target entity description: KUL is a prominent Catholic university in Lublin, Poland, known for its strong tradition in the humanities, social sciences, and theology.
  • A. KUL
    KUL is the IATA airport code for Kuala Lumpur International Airport, the main international gateway serving Malaysia’s capital region.
  • B. KAL
    KAL is the ICAO airline designator used to identify Korean Air in international aviation operations.
  • C. KLu
    KLu is the commonly used abbreviation for the Royal Netherlands Air Force, the aerial warfare branch of the Dutch armed forces.
  • D. KLE
    KLE is the vehicle registration code for the district of Cleves (Kleve) in the German state of North Rhine-Westphalia.
  • E. KLS
    KLS is a research center at Kiel University focused on interdisciplinary life science studies, including molecular biology, medicine, and environmental sciences.
  • 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_69ca84e6493081909cf58c8d42ea856b completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb39864188190a2d8c0ee911f00c2 completed April 2, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1e43b2de881909e00f6701d1c7b54 completed April 5, 2026, 4:25 a.m.
NEDg Description generation batch_69d1e5204f748190b1f56ee5469828a2 completed April 5, 2026, 4:29 a.m.
NED2 Entity disambiguation (via description) batch_69d1e598243481909278cb3c911ce3db completed April 5, 2026, 4:31 a.m.
Created at: March 30, 2026, 8:35 p.m.