Triple

T9758077
Position Surface form Disambiguated ID Type / Status
Subject Fraunhofer Institute for Wood Research WKI E236600 entity
Predicate abbreviation P43 FINISHED
Object WKI
WKI is a German research institute specializing in wood science and wood-based materials, operating under the Fraunhofer Society.
 E818151 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: WKI | Statement: [Fraunhofer Institute for Wood Research WKI, abbreviation, WKI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WKI
Context triple: [Fraunhofer Institute for Wood Research WKI, abbreviation, WKI]
  • A. WMKI
    WMKI is the ICAO airport code for Sultan Azlan Shah Airport in Ipoh, Malaysia.
  • B. WIQ
    WIQ is the National Rail station code for West India Quay Docklands Light Railway station in London.
  • C. WIJJ
    WIJJ was the former ICAO airport code assigned to JOG, the airport serving Yogyakarta, Indonesia.
  • D. KJWY
    KJWY is the ICAO airport code for Mid-Way Regional Airport, a public airport serving the Midlothian and Waxahachie area in Texas, United States.
  • E. KBWI
    KBWI is the ICAO airport code for Baltimore/Washington International Thurgood Marshall Airport, a major commercial airport serving the Baltimore–Washington metropolitan area in the United States.
  • 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: WKI
Triple: [Fraunhofer Institute for Wood Research WKI, abbreviation, WKI]
Generated description
WKI is a German research institute specializing in wood science and wood-based materials, operating under the Fraunhofer Society.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WKI
Target entity description: WKI is a German research institute specializing in wood science and wood-based materials, operating under the Fraunhofer Society.
  • A. WMKI
    WMKI is the ICAO airport code for Sultan Azlan Shah Airport in Ipoh, Malaysia.
  • B. WIQ
    WIQ is the National Rail station code for West India Quay Docklands Light Railway station in London.
  • C. WIJJ
    WIJJ was the former ICAO airport code assigned to JOG, the airport serving Yogyakarta, Indonesia.
  • D. KJWY
    KJWY is the ICAO airport code for Mid-Way Regional Airport, a public airport serving the Midlothian and Waxahachie area in Texas, United States.
  • E. KBWI
    KBWI is the ICAO airport code for Baltimore/Washington International Thurgood Marshall Airport, a major commercial airport serving the Baltimore–Washington metropolitan area in the United States.
  • 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_69ca84d64f6c8190a4ed4e9f5936eda5 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda046a4048190a2c66321a9911817 completed April 1, 2026, 10:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1b035c32c8190ad65f3d57d53a317 completed April 5, 2026, 12:43 a.m.
NEDg Description generation batch_69d1b0dde93881908fcec28de9cfa99d completed April 5, 2026, 12:46 a.m.
NED2 Entity disambiguation (via description) batch_69d1b1bbe6108190af17b75f79c0f465 completed April 5, 2026, 12:50 a.m.
Created at: March 30, 2026, 8:24 p.m.