Triple
T16375741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert "Sput" Searight |
E397674
|
entity |
| Predicate | associatedAct |
P37
|
FINISHED |
| Object | Toto |
E735671
|
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: Toto | Statement: [Robert "Sput" Searight, associatedAct, Toto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toto Context triple: [Robert "Sput" Searight, associatedAct, Toto]
-
A.
Toto
Toto is the small, loyal dog who accompanies Dorothy Gale on her adventures in L. Frank Baum’s "The Wonderful Wizard of Oz" and its adaptations.
-
B.
Toto
Toto is a local government area in Nasarawa State, Nigeria, known for its predominantly rural communities and agrarian-based economy.
-
C.
Toto
Toto is the nickname of Italian former footballer Salvatore Schillaci, famed for his standout goal-scoring performance at the 1990 FIFA World Cup.
-
D.
Toto
chosen
Toto is an American rock band best known for hits like "Africa," "Rosanna," and "Hold the Line," blending pop, rock, and jazz influences.
-
E.
Doc the Tiger
Doc the Tiger is the costumed tiger mascot representing Towson University's women's basketball team and broader athletic programs.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e319d79df8819087285b9457b7bdb6 |
completed | April 18, 2026, 5:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00356406208190a9cedc1de2ab4e07 |
completed | May 10, 2026, 7:36 a.m. |
Created at: April 10, 2026, 5:08 a.m.