Triple
T10015433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Leeson |
E199479
|
entity |
| Predicate | voiceRoleIn |
P1668
|
FINISHED |
| Object | K-9 (TV series) |
E71121
|
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: K-9 (TV series) | Statement: [John Leeson, voiceRoleIn, K-9 (TV series)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: K-9 (TV series) Context triple: [John Leeson, voiceRoleIn, K-9 (TV series)]
-
A.
K-9
chosen
K-9 is a robotic dog from the Doctor Who universe, known as a loyal, intelligent companion equipped with advanced technology and weaponry.
-
B.
K9K
K9K is a Canadian postal code prefix assigned to part of the city of Peterborough in Ontario.
-
C.
K-9 Unit
The K-9 Unit is a specialized police division that uses trained dogs to assist officers in tasks such as tracking suspects, detecting drugs or explosives, and conducting search and rescue operations.
-
D.
K-9 and Company
K-9 and Company is a British science-fiction television series set in the Doctor Who universe, featuring the Doctor’s robotic dog K-9 and former companion Sarah Jane Smith in their own adventures.
-
E.
K-99
K-99 is a north–south state highway running through eastern Kansas, connecting several small towns and rural 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_69ca8315a1a08190ab310f25620f362b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd4ad3348190bae03cd37c787674 |
completed | April 2, 2026, 1:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2821b22488190913d743bc40a4c8e |
completed | April 5, 2026, 3:39 p.m. |
Created at: March 30, 2026, 8:52 p.m.