Triple

T9449061
Position Surface form Disambiguated ID Type / Status
Subject Mauretania Caesariensis E227838 entity
Predicate hadImportantCity P22141 FINISHED
Object Tipasa E671843 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: Tipasa | Statement: [Mauretania Caesariensis, hadImportantCity, Tipasa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tipasa
Context triple: [Mauretania Caesariensis, hadImportantCity, Tipasa]
  • A. Tipaza chosen
    Tipaza is a coastal town in northern Algeria known for its significant Roman archaeological ruins and scenic Mediterranean setting.
  • B. Tuspa
    Tuspa is an alternative name for Tushpa, the ancient capital city of the Urartian kingdom located near modern-day Lake Van in eastern Turkey.
  • C. Tapaz
    Tapaz is a landlocked agricultural municipality in the province of Capiz on Panay Island in the Philippines, known for its rural landscapes and river valleys.
  • D. Tiscamanita
    Tiscamanita is a small village on the island of Fuerteventura in Spain’s Canary Islands, known for its traditional windmills and rural character.
  • E. Tamasopo
    Tamasopo is a small town in the Huasteca Potosina region of San Luis Potosí, Mexico, known for its lush landscapes and popular nearby waterfalls and natural swimming areas.
  • 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_69ca8439f8bc8190997f2ef40c9f0bc2 completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd7f64fdc88190aec27793bfe62d77 completed April 1, 2026, 8:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1106f38448190bfdc289eb987e33f completed April 4, 2026, 1:21 p.m.
Created at: March 30, 2026, 7:51 p.m.