Triple

T2320497
Position Surface form Disambiguated ID Type / Status
Subject Sd.Kfz. 251 E51167 entity
Predicate manufacturer P490 FINISHED
Object Hanomag
Hanomag was a German engineering and vehicle manufacturing company best known for producing military half-tracks and civilian tractors in the first half of the 20th century.
 E256148 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: Hanomag | Statement: [Sd.Kfz. 251, manufacturer, Hanomag]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hanomag
Context triple: [Sd.Kfz. 251, manufacturer, Hanomag]
  • A. Yuasa
    Yuasa is a historic coastal town in Japan renowned as the birthplace of traditional soy sauce production.
  • B. Nisshoki
    Nisshoki, more commonly known as the Hinomaru, is the national flag of Japan featuring a red sun disc centered on a white field.
  • C. Hama
    Hama is a major city in west-central Syria, historically known for its ancient waterwheels (norias) on the Orontes River and its role as an important agricultural and industrial center.
  • D. Yoshimura
    Yoshimura is a Japanese surname borne by various notable individuals across fields such as politics, sports, and the arts.
  • E. Eichig
    Eichig is a small locality that forms part of the town of Lichtenfels in the Upper Franconia region of Bavaria, Germany.
  • 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: Hanomag
Triple: [Sd.Kfz. 251, manufacturer, Hanomag]
Generated description
Hanomag was a German engineering and vehicle manufacturing company best known for producing military half-tracks and civilian tractors in the first half of the 20th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hanomag
Target entity description: Hanomag was a German engineering and vehicle manufacturing company best known for producing military half-tracks and civilian tractors in the first half of the 20th century.
  • A. Yuasa
    Yuasa is a historic coastal town in Japan renowned as the birthplace of traditional soy sauce production.
  • B. Nisshoki
    Nisshoki, more commonly known as the Hinomaru, is the national flag of Japan featuring a red sun disc centered on a white field.
  • C. Hama
    Hama is a major city in west-central Syria, historically known for its ancient waterwheels (norias) on the Orontes River and its role as an important agricultural and industrial center.
  • D. Yoshimura
    Yoshimura is a Japanese surname borne by various notable individuals across fields such as politics, sports, and the arts.
  • E. Eichig
    Eichig is a small locality that forms part of the town of Lichtenfels in the Upper Franconia region of Bavaria, Germany.
  • 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_69a88b074b908190ae983dbca7757d88 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc632474c8190972b4611a3a4ff8f completed March 7, 2026, 6:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae896911908190b53954dbf854cc18 completed March 9, 2026, 8:48 a.m.
NEDg Description generation batch_69ae8d2dcc8081908d4274b2287ff2b8 completed March 9, 2026, 9:04 a.m.
NED2 Entity disambiguation (via description) batch_69ae8d786a648190acf0a14e0d4a120c completed March 9, 2026, 9:06 a.m.
Created at: March 4, 2026, 7:49 p.m.