Triple
T13776368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diane Chambers |
E331016
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Carla Tortelli |
E330418
|
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: Carla Tortelli | Statement: [Diane Chambers, associatedWith, Carla Tortelli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carla Tortelli Context triple: [Diane Chambers, associatedWith, Carla Tortelli]
-
A.
Carla Tortelli
chosen
Carla Tortelli is a sharp-tongued, tough, and fiercely loyal waitress on the classic American sitcom "Cheers."
-
B.
Maria Pia Calzone
Maria Pia Calzone is an Italian actress known for her work in film and television, including prominent roles in contemporary Italian cinema and series.
-
C.
Carla Leone
Carla Leone was the wife of renowned Italian film director Sergio Leone.
-
D.
Lucinda Tortelli
Lucinda Tortelli is one of Carla Tortelli’s children on the classic television sitcom "Cheers."
-
E.
Maria Colacurcio
Maria Colacurcio is an American technology executive and entrepreneur best known as a co-founder of Smartsheet and for her leadership roles in data-driven workplace equity and HR tech companies.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0238bdbc8190a946e6e5431632a5 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a2df73c8190aeb6f472ac7ebeed |
completed | May 8, 2026, 5:52 a.m. |
Created at: April 9, 2026, 10:10 p.m.