Triple
T12346380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Todd Komarnicki |
E294365
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Sully |
E62647
|
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: Sully | Statement: [Todd Komarnicki, notableWork, Sully]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sully Context triple: [Todd Komarnicki, notableWork, Sully]
-
A.
Sully
Sully is a coastal village and community in the Vale of Glamorgan, Wales, known for its seaside location and residential character.
-
B.
Sully
chosen
"Sully" is a biographical drama film starring Tom Hanks as pilot Chesley "Sully" Sullenberger, depicting the emergency landing of US Airways Flight 1549 on the Hudson River.
-
C.
Sully
Sully is the given name of François Sully Prudhomme, a French individual identifiable by this personal name.
-
D.
Sully
Sully is a masculine given name, often used as a diminutive or nickname for names like Sullivan.
-
E.
The Finest Hours
The Finest Hours is a 2016 American disaster drama film depicting the true story of a daring U.S. Coast Guard rescue off the New England coast during a brutal 1952 nor’easter.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f7a4a448190aa70d66dc2f406c1 |
completed | April 10, 2026, 6:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62aac9edc8190ab1740d23af1b250 |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:53 p.m.