Triple
T23389605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lotso |
E593975
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Lotso |
—
|
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: Lotso | Statement: [Lotso, alsoKnownAs, Lotso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lotso Context triple: [Lotso, alsoKnownAs, 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.
Lotan
Lotan is a multi-headed sea serpent or dragon from ancient Northwest Semitic mythology, often associated with chaos and defeated by the storm god.
-
C.
Lotan
Lotan is a biblical figure listed in Genesis as a descendant of Seir the Horite and a chief of the Horite clans in the region of Edom.
-
D.
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.
-
E.
Tanto
Tanto was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
- 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_69e25d2754fc819085deea939bde60ab |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a49a7c14819082aab826715976c5 |
completed | April 29, 2026, 6:26 a.m. |
Created at: April 17, 2026, 5:35 p.m.