Triple
T19653701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cybermen |
E471878
|
entity |
| Predicate | homeworld |
P2102
|
FINISHED |
| Object | Telos |
—
|
NE NERFINISHED |
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: Telos | Statement: [Cybermen, homeworld, Telos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Telos Context triple: [Cybermen, homeworld, Telos]
-
A.
Telos
chosen
Telos is an icy, subterranean planet in the Doctor Who universe best known as a major stronghold and tomb world of the Cybermen.
-
B.
Telendos
Telendos is a small, tranquil Greek island in the Dodecanese known for its rugged cliffs, quiet beaches, and appeal to climbers and hikers.
-
C.
Kossil
Kossil is a high-ranking priestess and antagonist in Ursula K. Le Guin’s Earthsea novel "The Tombs of Atuan," known for her cruelty, jealousy, and opposition to the protagonist Tenar.
-
D.
Telaria
Telaria was a video advertising and monetization technology company specializing in connected TV and premium video inventory.
-
E.
Atheras
Atheras is a mountainous region on the Greek island of Ikaria, known for its rugged terrain and scenic landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51395348190ac1416d46dfc6db0 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e641442994819085a7d9562bf0bdcd |
completed | April 20, 2026, 3:07 p.m. |
Created at: April 10, 2026, 1:44 p.m.