Triple

T8510380
Position Surface form Disambiguated ID Type / Status
Subject Taurica E201436 entity
Predicate hasColony P160 FINISHED
Object Theodosia E69993 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: Theodosia | Statement: [Taurica, hasColony, Theodosia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Theodosia
Context triple: [Taurica, hasColony, Theodosia]
  • A. Theodosia chosen
    Theodosia is a historic port city on the southeastern coast of Crimea, known for its long history as a trading center on the Black Sea.
  • B. Theodosia
    Theodosia is a feminine given name of Greek origin, historically associated with early Christian martyrs and later borne by notable women in American history.
  • C. Alexandretta
    Alexandretta, historically known as İskenderun, is a strategic port city on Turkey’s Mediterranean coast that has long been a focal point of regional trade and territorial disputes.
  • D. Naousa
    Naousa is a historic town in northern Greece known for its wine production, natural springs, and role in the Greek War of Independence.
  • E. Arsacia
    Arsacia is an alternative name historically used for the city of Rayy (near modern-day Tehran) in ancient Persia.
  • 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_69ca8320e5748190ac2c585a0bba8193 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe5e0cb1881909d1ff6ee9b3a65cc completed March 31, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce4e3faa0c81908533e9097ed29b26 completed April 2, 2026, 11:08 a.m.
Created at: March 30, 2026, 6:15 p.m.