Triple
T6969507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RRR |
E161566
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Alison Doody |
E317705
|
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: Alison Doody | Statement: [RRR, starring, Alison Doody]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alison Doody Context triple: [RRR, starring, Alison Doody]
-
A.
Alison Doody
chosen
Alison Doody is an Irish actress and former model best known for her role as Dr. Elsa Schneider in the film "Indiana Jones and the Last Crusade."
-
B.
Alison Brown
Alison Brown is a film industry professional best known for her role in founding the American animation company Blue Sky Studios.
-
C.
Alison O’Brien
Alison O’Brien is a film producer known for her work on the 2019 animated adaptation of The Addams Family.
-
D.
Alison Porter
Alison Porter is a central character in John Osborne’s play "Look Back in Anger," portrayed as the emotionally conflicted and long-suffering wife of the protagonist, Jimmy Porter.
-
E.
Michelle Mylett
Michelle Mylett is a Canadian actress best known for playing Katy on the comedy series "Letterkenny."
- 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db1649288190a52c7dab57b3c7dc |
completed | March 27, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8ac7b53488190a00978e936b563ac |
completed | March 29, 2026, 4:37 a.m. |
Created at: March 27, 2026, 2:30 p.m.