Triple

T57486
Position Surface form Disambiguated ID Type / Status
Subject Egypt E1136 entity
Predicate borderCountry P224 FINISHED
Object Libya E1495 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: Libya | Statement: [Egypt, borderCountry, Libya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Libya
Context triple: [Egypt, borderCountry, Libya]
  • A. Libya chosen
    Libya is a North African country on the Mediterranean coast, known for its vast desert landscapes, significant oil reserves, and history spanning from ancient civilizations to modern political upheavals.
  • B. Tunisia
    Tunisia is a North African country on the Mediterranean coast, known for its strategic location, ancient Carthaginian and Roman heritage, and role as a key battleground in World War II.
  • C. Algeria
    Algeria is a large North African country on the Mediterranean coast, known for its vast Sahara Desert regions, rich history, and significant role in both colonial and post-colonial geopolitics.
  • D. Syria
    Syria is a country in the Eastern Mediterranean region of Western Asia, known for its ancient civilizations, diverse cultural heritage, and protracted civil war since 2011.
  • E. Morocco
    Morocco is a North African country known for its rich blend of Arab, Berber, and European cultural influences, historic cities like Marrakech and Fez, and diverse landscapes ranging from Atlantic coastlines to the Atlas Mountains and Sahara Desert.
  • 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_69a248adc5b48190aa8db9fb092fb28a completed Feb. 28, 2026, 1:45 a.m.
NER Named-entity recognition batch_69a24b1bf2c081908f20e13939b713ff completed Feb. 28, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69a29176de5c819086e1cfec0a23d9d7 completed Feb. 28, 2026, 6:55 a.m.
Created at: Feb. 28, 2026, 1:50 a.m.