Triple

T1750262
Position Surface form Disambiguated ID Type / Status
Subject Freya radar E38422 entity
Predicate variant P4680 FINISHED
Object Freya-LZ
Freya-LZ was a later, improved version of the German World War II Freya early-warning radar system, offering enhanced range and performance for air defense.
 E196911 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: Freya-LZ | Statement: [Freya radar, variant, Freya-LZ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Freya-LZ
Context triple: [Freya radar, variant, Freya-LZ]
  • A. Lyn
    Lyn is a Norwegian football club based in Oslo with a long history in the country’s top divisions.
  • B. Fey
    Fey is the surname of American comedian, writer, actress, and producer Tina Fey, known for her work on "Saturday Night Live" and "30 Rock."
  • C. Fay
    Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
  • D. Zella
    Zella is an activewear and athleisure clothing brand known for its performance-focused yet stylish designs, sold at Nordstrom.
  • E. Zenia
    Zenia is a central, enigmatic and manipulative figure in Margaret Atwood's novel "The Robber Bride," whose disruptive influence profoundly affects the lives of three other women.
  • 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: Freya-LZ
Triple: [Freya radar, variant, Freya-LZ]
Generated description
Freya-LZ was a later, improved version of the German World War II Freya early-warning radar system, offering enhanced range and performance for air defense.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Freya-LZ
Target entity description: Freya-LZ was a later, improved version of the German World War II Freya early-warning radar system, offering enhanced range and performance for air defense.
  • A. Lyn
    Lyn is a Norwegian football club based in Oslo with a long history in the country’s top divisions.
  • B. Fey
    Fey is the surname of American comedian, writer, actress, and producer Tina Fey, known for her work on "Saturday Night Live" and "30 Rock."
  • C. Fay
    Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
  • D. Zella
    Zella is an activewear and athleisure clothing brand known for its performance-focused yet stylish designs, sold at Nordstrom.
  • E. Zenia
    Zenia is a central, enigmatic and manipulative figure in Margaret Atwood's novel "The Robber Bride," whose disruptive influence profoundly affects the lives of three other women.
  • 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_69a8862bdb2081908aefe831c8aa8017 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa64108a208190ae7190065818e42c completed March 6, 2026, 5:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada0e40cb88190953c639ee2464a54 completed March 8, 2026, 4:16 p.m.
NEDg Description generation batch_69ada1a2fb9481909d9ed587921ca6b6 completed March 8, 2026, 4:19 p.m.
NED2 Entity disambiguation (via description) batch_69ada4e28830819082ed7facee14587f completed March 8, 2026, 4:33 p.m.
Created at: March 4, 2026, 7:31 p.m.