Triple

T23063997
Position Surface form Disambiguated ID Type / Status
Subject Franklin C. Sibert E574980 entity
Predicate familyName P18 FINISHED
Object Sibert
Sibert is a surname most notably associated with Franklin C. Sibert, a U.S. Army lieutenant general who served in World War II.
 E1568594 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: Sibert | Statement: [Franklin C. Sibert, familyName, Sibert]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sibert
Context triple: [Franklin C. Sibert, familyName, Sibert]
  • A. Sibert
    Sibert is the given middle name of the renowned American novelist Willa Cather, known for her depictions of frontier life on the Great Plains.
  • B. Siebert
    Siebert is a surname most notably associated with Sonny Siebert, an American Major League Baseball pitcher active in the 1960s and 1970s.
  • C. Seibert
    Seibert is a German-origin surname borne by various notable individuals across fields such as sports, science, and the arts.
  • D. Mike Teavee
    Mike Teavee is a television-obsessed, video game–addicted boy whose bratty behavior and fixation on screens lead to his comically disastrous fate during Willy Wonka’s factory tour.
  • E. Postel
    Postel is a surname most prominently associated with Jon Postel, a pioneering computer scientist and key architect of the early Internet.
  • 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: Sibert
Triple: [Franklin C. Sibert, familyName, Sibert]
Generated description
Sibert is a surname most notably associated with Franklin C. Sibert, a U.S. Army lieutenant general who served in World War II.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sibert
Target entity description: Sibert is a surname most notably associated with Franklin C. Sibert, a U.S. Army lieutenant general who served in World War II.
  • A. Sibert
    Sibert is the given middle name of the renowned American novelist Willa Cather, known for her depictions of frontier life on the Great Plains.
  • B. Siebert
    Siebert is a surname most notably associated with Sonny Siebert, an American Major League Baseball pitcher active in the 1960s and 1970s.
  • C. Seibert
    Seibert is a German-origin surname borne by various notable individuals across fields such as sports, science, and the arts.
  • D. Mike Teavee
    Mike Teavee is a television-obsessed, video game–addicted boy whose bratty behavior and fixation on screens lead to his comically disastrous fate during Willy Wonka’s factory tour.
  • E. Postel
    Postel is a surname most prominently associated with Jon Postel, a pioneering computer scientist and key architect of the early Internet.
  • 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_69e245bd6e4c8190bb8942245b68cad5 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f189a1f49c81909db7e0473ec2bb1b completed April 29, 2026, 4:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c0aeae0d08190b2f75cd2b44a0403 completed May 19, 2026, 7:02 a.m.
NEDg Description generation batch_6a0c0c69b46481908026d0d065e3c926 completed May 19, 2026, 7:08 a.m.
NED2 Entity disambiguation (via description) batch_6a0c10498e2c8190ad4eb736a86e796e completed May 19, 2026, 7:24 a.m.
Created at: April 17, 2026, 3:55 p.m.