Triple
T22541225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Must Love Dogs |
E557292
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Sarah Nolan |
—
|
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: Sarah Nolan | Statement: [Must Love Dogs, character, Sarah Nolan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Nolan Context triple: [Must Love Dogs, character, Sarah Nolan]
-
A.
Sarah Nolan
chosen
Sarah Nolan is a fictional character best known as the recently divorced preschool teacher seeking love in the romantic comedy film "Must Love Dogs."
-
B.
Rachel O’Riordan
Rachel O’Riordan is a prominent theatre director and arts leader known for her innovative work in contemporary drama and for revitalizing major UK theatre institutions.
-
C.
Mary O'Riordan
Mary O'Riordan is an actress known for her role in the Irish historical drama film "The Wind That Shakes the Barley."
-
D.
Michelle McNally
Michelle McNally is the deaf-blind protagonist of the Indian drama film "Black," whose journey of education and self-discovery forms the emotional core of the story.
-
E.
Bríd Meaney
Bríd Meaney is the daughter of Irish actor Colm Meaney, known for his roles in "Star Trek" and numerous film and television productions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69e11e58662081909ae346ab384514ca |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f3251808190a72b849157854d8d |
completed | April 29, 2026, 1:30 a.m. |
Created at: April 16, 2026, 8:51 p.m.