Triple
T6590409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Williams |
E159338
|
entity |
| Predicate | memberOf |
P10
|
FINISHED |
| Object | Toto |
E115952
|
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: [Joseph Williams, memberOf, Toto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toto Context triple: [Joseph Williams, memberOf, Toto]
-
A.
Toto
Toto is the nickname of Italian former footballer Salvatore Schillaci, famed for his standout goal-scoring performance at the 1990 FIFA World Cup.
-
B.
Toto
chosen
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.
-
C.
Toto
Toto is a local government area in Nasarawa State, Nigeria, known for its predominantly rural communities and agrarian-based economy.
-
D.
TOM the Tiger
TOM the Tiger is the live Bengal tiger mascot that represents the University of Memphis Tigers athletic programs.
-
E.
Buggsy
Buggsy is the nickname of Martin "Buggsy" Goldstein, an American mobster associated with organized crime in the early 20th century.
- 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_69c688366ce8819083f8883983c0df92 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6aecc969c81909a6e15ebe8dd3f94 |
completed | March 27, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cbb568508190b9da3475d1620ed5 |
completed | March 27, 2026, 6:25 p.m. |
Created at: March 27, 2026, 1:55 p.m.