Triple
T9336905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Currie |
E224665
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Tony |
E265285
|
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: Tony | Statement: [Tony Currie, givenName, Tony]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tony Context triple: [Tony Currie, givenName, Tony]
-
A.
Tony
Tony is a fictional character from the animated series "Wild Target," known for his adventurous role within the show's ensemble cast.
-
B.
Tony
Tony is the idealistic young protagonist of the musical *West Side Story*, whose forbidden love for Maria drives the story’s modern retelling of *Romeo and Juliet* in 1950s New York.
-
C.
Tony
Tony is the NATO reporting name for the Japanese World War II Kawasaki Ki-61 fighter aircraft.
-
D.
Tony
chosen
Tony is a common masculine given name, often used as a diminutive of Anthony or Antonio.
-
E.
Tony
Tony is the central romantic lead in the musical "The Most Happy Fella," an aging Italian-American vintner whose love story drives the plot.
- 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_69ca84286fcc81909f6e7fd7a7e862a2 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd37f161e481908e23c1ec7e5fcf97 |
completed | April 1, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0e3d8316081908cb9ea36eb069c2d |
completed | April 4, 2026, 10:11 a.m. |
Created at: March 30, 2026, 7:40 p.m.