Triple

T301562
Position Surface form Disambiguated ID Type / Status
Subject Tebu E6206 entity
Predicate country P26 FINISHED
Object Chad E20731 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: Chad | Statement: [Tebu, country, Chad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chad
Context triple: [Tebu, country, Chad]
  • A. Chad chosen
    Chad is a landlocked country in north-central Africa known for its ethnic and linguistic diversity, vast desert regions, and significant oil reserves.
  • B. Sudan
    Sudan is a large Northeast African country along the Nile River, known for its diverse cultures, ancient Nubian history, and a modern history marked by civil conflict and the secession of South Sudan.
  • C. Cameroon
    Cameroon is a Central African country known for its cultural and linguistic diversity, varied geography from coast to rainforest and savanna, and a mixed French-English colonial heritage.
  • D. South Sudan
    South Sudan is a landlocked country in East-Central Africa that gained independence from Sudan in 2011 and has since faced ongoing political instability and humanitarian challenges.
  • E. Niger
    Niger is a landlocked West African country in the Sahel region, known for its vast desert landscapes, uranium resources, and predominantly rural population.
  • 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_69a2e79230508190b912ecb555aae17e completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2ea0dd1dc8190aecd5afdeb2fd74b completed Feb. 28, 2026, 1:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69a457fa35988190851216c84ad63232 completed March 1, 2026, 3:15 p.m.
Created at: Feb. 28, 2026, 1:06 p.m.