Triple
T15469884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coffee Town |
E372130
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Sam Daly |
E988579
|
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: Sam Daly | Statement: [Coffee Town, castMember, Sam Daly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Daly Context triple: [Coffee Town, castMember, Sam Daly]
-
A.
Sam Daly
chosen
Sam Daly is an American actor known for roles in film and television and as the son of actor Tim Daly.
-
B.
Edward Daly
Edward Daly was an Irish Catholic priest and later Bishop of Derry, widely recognized for his compassionate and courageous role during the 1972 Bloody Sunday shootings in Northern Ireland.
-
C.
Edward Daly
Edward Daly was an Irish republican leader and commandant in the 1916 Easter Rising who was executed by British forces for his role in the rebellion.
-
D.
Sam Dolan
Sam Dolan is the central protagonist of the film "Double Feature," around whom the story’s main events and character dynamics revolve.
-
E.
Henry Dailey
Henry Dailey is the retired horse trainer who becomes the mentor and caretaker of the wild stallion and its young rider in Walter Farley’s "The Black Stallion" series.
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f6b49788190b270fdfe92646842 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f7d79348190aba1889a7eb3d7c8 |
completed | May 10, 2026, 6:02 a.m. |
Created at: April 10, 2026, 3:33 a.m.