Triple
T23470533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Skyrme |
E569214
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Tony |
—
|
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: Tony | Statement: [Tony Skyrme, givenName, Tony]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tony Context triple: [Tony Skyrme, givenName, Tony]
-
A.
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.
-
B.
Tony
Tony is the humanoid robot protagonist of Isaac Asimov’s science fiction short story “Satisfaction Guaranteed,” designed to interact closely with humans and explore the emotional and ethical implications of human–robot relationships.
-
C.
Tony
Tony is a fictional character appearing in the Marx Brothers comedy film "A Day at the Races."
-
D.
Tony
Tony is a kind-hearted Chicago police officer who briefly dates Fiona Gallagher in the U.S. version of the TV series "Shameless."
-
E.
Tony
Tony is a fictional character appearing in the romantic comedy film "Come September."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69e2458ebd808190b3298163132cfb0b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a6ff8dc0819086961b1f07030d9c |
completed | April 29, 2026, 6:36 a.m. |
Created at: April 17, 2026, 5:54 p.m.