Triple
T15636577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jessica Jones |
E375959
|
entity |
| Predicate | composer |
P1361
|
FINISHED |
| Object | Sean Callery |
E272604
|
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: Sean Callery | Statement: [Jessica Jones, composer, Sean Callery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sean Callery Context triple: [Jessica Jones, composer, Sean Callery]
-
A.
Sean Callery
chosen
Sean Callery is an Emmy-winning television and film composer best known for his suspenseful, atmospheric scores for series such as 24, Homeland, and Jessica Jones.
-
B.
Curtis Callan
Curtis Callan is an American theoretical physicist known for his influential work in quantum field theory, string theory, and the renormalization group.
-
C.
Ryan Sweeney
Ryan Sweeney is a former American Major League Baseball outfielder who played for teams including the Chicago White Sox, Oakland Athletics, and Boston Red Sox.
-
D.
Jason Crouse
Jason Crouse is a fictional defense investigator and love interest of Alicia Florrick on the legal drama television series "The Good Wife."
-
E.
Jason McCullough
Jason McCullough is the quick-witted, reluctant sheriff protagonist of the comedic Western film "Support Your Local Sheriff!"
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eba51f08190ac5d9de7fc89405a |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f4923ac8190a03fe1f2c878c27e |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:14 a.m.