Triple

T16695118
Position Surface form Disambiguated ID Type / Status
Subject Loricifera E405694 entity
Predicate describedBy P264 FINISHED
Object Reinhardt Kristensen E1206447 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: Reinhardt Kristensen | Statement: [Loricifera, describedBy, Reinhardt Kristensen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reinhardt Kristensen
Context triple: [Loricifera, describedBy, Reinhardt Kristensen]
  • A. Reinhardt Kristensen chosen
    Reinhardt Kristensen is a Danish zoologist and invertebrate biologist known for discovering and describing several microscopic animal groups, including the phylum Micrognathozoa.
  • B. Ole Christensen
    Ole Christensen is a Danish mathematician known for his contributions to functional analysis and frame theory.
  • C. Thue Christiansen
    Thue Christiansen was a Greenlandic teacher, artist, and politician best known for creating Greenland’s national flag.
  • D. Jorgen Holmboe
    Jorgen Holmboe was a Norwegian-American meteorologist known for his contributions to dynamic meteorology and weather forecasting theory.
  • E. Thorvald Jørgensen
    Thorvald Jørgensen was a Danish architect best known for his prominent public and ecclesiastical buildings in Copenhagen in the late 19th and early 20th centuries.
  • 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_69d8838db21081909589220fd71440a4 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37eacfa788190a8d2058f96c0d445 completed April 18, 2026, 12:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0139e5f5988190a87b62d32bfb32fa completed May 11, 2026, 2:07 a.m.
Created at: April 10, 2026, 5:19 a.m.