Triple
T16283857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edie Ichioka |
E395336
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Laika |
E307422
|
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: Laika | Statement: [Edie Ichioka, employer, Laika]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laika Context triple: [Edie Ichioka, employer, Laika]
-
A.
Laika
Laika was a Soviet space dog who became the first living creature to orbit Earth, marking a pivotal moment in the early Space Race.
-
B.
Laika
chosen
Laika is an American stop-motion animation studio renowned for visually distinctive, critically acclaimed films such as Coraline, ParaNorman, and Kubo and the Two Strings.
-
C.
Fido
Fido is a lesser-known companion character associated with the American folk hero Paul Bunyan in tall tales of the lumberjack’s adventures.
-
D.
Fido
Fido is a 2006 Canadian zombie comedy film in which Carrie-Anne Moss plays a lead role in a 1950s-style world where domesticated zombies serve humans.
-
E.
Fido
Fido is a Canadian mobile phone service provider known for offering wireless plans and devices, primarily targeting value-conscious consumers.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24912c5808190a0d9c9f491315068 |
completed | April 17, 2026, 2:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0017c8f51c8190b73cdf2834eda57f |
completed | May 10, 2026, 5:29 a.m. |
Created at: April 10, 2026, 5:05 a.m.