Triple

T9182190
Position Surface form Disambiguated ID Type / Status
Subject Kharkiv National University of Radioelectronics E220357 entity
Predicate abbreviation P43 FINISHED
Object KhNURE
KhNURE is a leading Ukrainian technical university in Kharkiv specializing in radio electronics, information technologies, and related engineering fields.
 E784191 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: KhNURE | Statement: [Kharkiv National University of Radioelectronics, abbreviation, KhNURE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KhNURE
Context triple: [Kharkiv National University of Radioelectronics, abbreviation, KhNURE]
  • A. HKNW
    HKNW is the ICAO airport code assigned to Wilson Airport in Nairobi, Kenya.
  • B. Knv
    Knv is the station code for Knivsta railway station in Sweden.
  • C. KNO
    KNO is the IATA airport code for Kualanamu International Airport serving Medan and the surrounding region in North Sumatra, Indonesia.
  • D. NKUA
    NKUA is a major public research university in Athens, Greece, and one of the oldest higher education institutions in the modern Greek state.
  • E. KND
    KND is the abbreviated name for the animated television series "Codename: Kids Next Door," which follows a group of child operatives fighting adult tyranny from their high-tech treehouse headquarters.
  • 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: KhNURE
Triple: [Kharkiv National University of Radioelectronics, abbreviation, KhNURE]
Generated description
KhNURE is a leading Ukrainian technical university in Kharkiv specializing in radio electronics, information technologies, and related engineering fields.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KhNURE
Target entity description: KhNURE is a leading Ukrainian technical university in Kharkiv specializing in radio electronics, information technologies, and related engineering fields.
  • A. HKNW
    HKNW is the ICAO airport code assigned to Wilson Airport in Nairobi, Kenya.
  • B. Knv
    Knv is the station code for Knivsta railway station in Sweden.
  • C. KNO
    KNO is the IATA airport code for Kualanamu International Airport serving Medan and the surrounding region in North Sumatra, Indonesia.
  • D. NKUA
    NKUA is a major public research university in Athens, Greece, and one of the oldest higher education institutions in the modern Greek state.
  • E. KND
    KND is the abbreviated name for the animated television series "Codename: Kids Next Door," which follows a group of child operatives fighting adult tyranny from their high-tech treehouse headquarters.
  • 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_69ca83e589948190ac9907819db11ddf completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccc25379008190b8ca047efe3bb7bb completed April 1, 2026, 6:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05c0eeb0c8190a5a22c662726805c completed April 4, 2026, 12:32 a.m.
NEDg Description generation batch_69d05cec61908190ae783af982db4170 completed April 4, 2026, 12:35 a.m.
NED2 Entity disambiguation (via description) batch_69d05dbf1ce081908d5c8168a9315942 completed April 4, 2026, 12:39 a.m.
Created at: March 30, 2026, 7:23 p.m.