Triple
T15267053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gloria Talbott |
E364925
|
entity |
| Predicate | appearedIn |
P795
|
FINISHED |
| Object | Lassie |
E500258
|
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: Lassie | Statement: [Gloria Talbott, appearedIn, Lassie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lassie Context triple: [Gloria Talbott, appearedIn, Lassie]
-
A.
Lassie
chosen
Lassie is a famous fictional Rough Collie dog character best known as the heroic star of a long-running American television series and numerous films and books.
-
B.
Laika
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.
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.
-
D.
Balto
Balto is the famous sled dog who led his team on the final leg of the 1925 serum run to Nome, Alaska, becoming a symbol of bravery and endurance.
-
E.
Beasley the Dog
Beasley the Dog was the canine actor best known for playing the slobbery Dogue de Bordeaux partner to Tom Hanks in the 1989 film "Turner & Hooch."
- 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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00851c5b88190a296b6a105d3ee30 |
completed | April 15, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee600340c8190a1888d35c2c1bc86 |
completed | May 9, 2026, 7:45 a.m. |
Created at: April 10, 2026, 3:14 a.m.