Triple

T8862380
Position Surface form Disambiguated ID Type / Status
Subject Georg-Hans Reinhardt E210922 entity
Predicate militaryBranch P253 FINISHED
Object Heer E9485 NE FINISHED

How this triple was built (2 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: Heer | Statement: [Georg-Hans Reinhardt, militaryBranch, Heer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Heer
Context triple: [Georg-Hans Reinhardt, militaryBranch, Heer]
  • A. Heer
    Heer is the tragic heroine of the classic Punjabi romantic epic "Heer Ranjha," renowned as a symbol of eternal love and devotion.
  • B. Heer chosen
    The Heer was the land-based component of Nazi Germany’s armed forces, serving as its primary army during World War II.
  • C. Heriz
    Heriz is a renowned carpet-weaving region in northwestern Iran, famous for its durable hand-knotted rugs featuring bold geometric medallion designs.
  • D. Heers
    Heers is a rural municipality in the Belgian province of Limburg, known for its agricultural landscape and historic villages.
  • E. Henreid
    Henreid is the surname of Paul Henreid, the Austrian-born actor and director best known for his roles in classic Hollywood films such as "Casablanca" and "Now, Voyager."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca838bbddc8190ab546d737e5d350f completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc610263048190931bb2c3ac573a08 completed April 1, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfa0c248108190815d593f44029183 completed April 3, 2026, 11:13 a.m.
Created at: March 30, 2026, 6:50 p.m.