Triple
T9762177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sunnyside Daycare |
E236695
|
entity |
| Predicate | ownerInStory |
P61752
|
FINISHED |
| Object | Lotso |
E236693
|
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: Lotso | Statement: [Sunnyside Daycare, ownerInStory, Lotso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lotso Context triple: [Sunnyside Daycare, ownerInStory, Lotso]
-
A.
Lotso
chosen
Lotso is the strawberry-scented teddy bear who serves as the main antagonist in Pixar's animated film Toy Story 3.
-
B.
Lakitu
Lakitu is a recurring cloud-riding Koopa in the Super Mario series known for hovering above the player and attacking by throwing Spiny eggs.
-
C.
Tanto
Tanto was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
-
D.
Liotta
Liotta is an Italian-origin surname most famously associated with American actor Ray Liotta, known for his roles in films like "Goodfellas."
-
E.
Lotha Hoho
Lotha Hoho is the apex traditional and socio-political tribal council representing the interests and governance of the Lotha Naga community in Nagaland, India.
- 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_69ca84d64f6c8190a4ed4e9f5936eda5 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda04c70108190a8ed09eb6f2a124e |
completed | April 1, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1c41022908190a5f55291a2323691 |
completed | April 5, 2026, 2:08 a.m. |
Created at: March 30, 2026, 8:25 p.m.