Triple

T7219021
Position Surface form Disambiguated ID Type / Status
Subject Second Reich E150208 entity
Predicate hadColony P160 FINISHED
Object Togoland E41698 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: Togoland | Statement: [Second Reich, hadColony, Togoland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Togoland
Context triple: [Second Reich, hadColony, Togoland]
  • A. Togo chosen
    Togo is a small West African country on the Gulf of Guinea, known for its diverse cultures, coastal capital Lomé, and history as a former French colony.
  • B. Equatorial Guinea
    Equatorial Guinea is a small Central African country on the Atlantic coast, known for its significant oil reserves and unique status as the only African nation where Spanish is an official language.
  • C. Gabon
    Gabon is a Central African country on the Atlantic coast, known for its equatorial rainforests, rich biodiversity, and significant oil reserves.
  • D. Guinea
    Guinea is a West African country on the Atlantic coast known for its rich mineral resources, diverse ethnic groups, and role as a major producer of bauxite.
  • E. Gaboni
    Gaboni is a small locality in southern Poland situated near the Beskid Sądecki mountain range, serving as a starting point for hikes to nearby peaks such as Przehyba.
  • 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_69c687effb44819092b95d07d0368c9f completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6e9b045c48190b27b2d6f7c11026f completed March 27, 2026, 8:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7d38423bc8190aaf4ee3940813d33 completed March 28, 2026, 1:11 p.m.
Created at: March 27, 2026, 2:53 p.m.