Triple
T12620586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank |
E301367
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Franco |
E343435
|
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: Franco | Statement: [Frank, relatedName, Franco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Franco Context triple: [Frank, relatedName, Franco]
-
A.
Franco
chosen
Franco is the given name of Franco Harris, the Hall of Fame NFL running back best known for the “Immaculate Reception” with the Pittsburgh Steelers.
-
B.
Benito
Benito is the given name of Benito Mussolini, the Italian dictator who founded and led the National Fascist Party and ruled Italy from the 1920s until 1943.
-
C.
Ferdinande
Ferdinande is the given name of Archduchess Auguste Ferdinande of Austria, a 19th-century member of the Habsburg-Lorraine dynasty.
-
D.
Francesco Francia
Francesco Francia was an Italian Renaissance painter and goldsmith from Bologna, known for his refined religious altarpieces and influential role in the Bolognese school of painting.
-
E.
Francois
Francois is the given first name of South African rugby union scrum-half Faf de Klerk.
- 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_69d7bdeaf49c8190b13800111fa77ea3 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d960c75c9c819092265ebc2b39f21d |
completed | April 10, 2026, 8:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ed6f79881908872c644a9789f04 |
completed | May 2, 2026, 8:30 p.m. |
Created at: April 9, 2026, 5:13 p.m.