Triple

T3195112
Position Surface form Disambiguated ID Type / Status
Subject Kea E66916 entity
Predicate formerName P65 FINISHED
Object Ceos E291383 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: Ceos | Statement: [Kea, formerName, Ceos]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ceos
Context triple: [Kea, formerName, Ceos]
  • A. Poros chosen
    Poros is a small Greek island in the Saronic Gulf known for its pine-covered hills, neoclassical town, and popularity as a nearby getaway from Athens.
  • B. Telos
    Telos is an icy, subterranean planet in the Doctor Who universe best known as a major stronghold and tomb world of the Cybermen.
  • C. Loggos
    Loggos is a small, picturesque coastal village on the Greek island of Paxos, known for its harbor, traditional tavernas, and relaxed atmosphere.
  • D. Cybus Industries
    Cybus Industries is a powerful fictional technology conglomerate in the Doctor Who universe, best known for creating an alternate-universe version of the Cybermen.
  • E. Leonardo DRS
    Leonardo DRS is a U.S.-based defense and technology company that provides advanced military electronics, integrated systems, and support services to armed forces and government customers.
  • 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_69ad8588ba18819086a10951c32ecb80 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada7163c6c8190b26b05b66740264d completed March 8, 2026, 4:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24bb2c9908190b3abc395537e22ac completed March 12, 2026, 5:14 a.m.
Created at: March 8, 2026, 3:07 p.m.