Triple

T18603820
Position Surface form Disambiguated ID Type / Status
Subject Atlantic petrel E454686 entity
Predicate describedBy P264 FINISHED
Object Schlegel
Schlegel was a 19th-century zoologist and taxonomist known for formally describing various animal species, including several seabirds.
 E117466 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: Schlegel | Statement: [Atlantic petrel, describedBy, Schlegel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Schlegel
Context triple: [Atlantic petrel, describedBy, Schlegel]
  • A. Schlegel
    Schlegel is a German surname most notably associated with the influential Romantic-era literary critics and philosophers August Wilhelm Schlegel and Friedrich Schlegel.
  • B. Schlegelberger
    Schlegelberger is a German surname most notably associated with Franz Schlegelberger, a high-ranking Nazi-era jurist and acting Reich Minister of Justice.
  • C. Meyer-Hetling
    Meyer-Hetling is a German surname most notably associated with Konrad Meyer-Hetling, an agronomist and SS officer involved in Nazi settlement planning.
  • D. Spangenberg
    Spangenberg is a small town in Germany, historically situated within the region of Westphalia.
  • E. Hufstedler
    Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
  • 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: Schlegel
Triple: [Atlantic petrel, describedBy, Schlegel]
Generated description
Schlegel was a 19th-century zoologist and taxonomist known for formally describing various animal species, including several seabirds.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Schlegel
Target entity description: Schlegel was a 19th-century zoologist and taxonomist known for formally describing various animal species, including several seabirds.
  • A. Schlegel chosen
    Schlegel is a German surname most notably associated with the influential Romantic-era literary critics and philosophers August Wilhelm Schlegel and Friedrich Schlegel.
  • B. Schlegelberger
    Schlegelberger is a German surname most notably associated with Franz Schlegelberger, a high-ranking Nazi-era jurist and acting Reich Minister of Justice.
  • C. Meyer-Hetling
    Meyer-Hetling is a German surname most notably associated with Konrad Meyer-Hetling, an agronomist and SS officer involved in Nazi settlement planning.
  • D. Spangenberg
    Spangenberg is a small town in Germany, historically situated within the region of Westphalia.
  • E. Hufstedler
    Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
  • F. None of above.

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_69d8d38bbe7c8190bdec3138e7d413c9 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e54752abec8190a5f4aa84abe8b240 completed April 19, 2026, 9:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a05038573588190a825b00e3f210c3f completed May 13, 2026, 11:04 p.m.
NEDg Description generation batch_6a05044722c88190bdeac36b602673e9 completed May 13, 2026, 11:07 p.m.
NED2 Entity disambiguation (via description) batch_6a0504deb488819086f3146141b3fb45 completed May 13, 2026, 11:10 p.m.
Created at: April 10, 2026, 11:45 a.m.