Triple
T17815742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allison Mack |
E444834
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Allison Mack |
—
|
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: Allison Mack | Statement: [Allison Mack, name, Allison Mack]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allison Mack Context triple: [Allison Mack, name, Allison Mack]
-
A.
Allison Mack
chosen
Allison Mack is an American actress best known for her role as Chloe Sullivan on the television series "Smallville."
-
B.
Melissa Cobb
Melissa Cobb is an American film producer best known for her work on major animated features, including the Kung Fu Panda franchise.
-
C.
Amy Acker
Amy Acker is an American actress best known for her roles in television series such as "Angel," "Person of Interest," and "Dollhouse."
-
D.
Tiffani Thiessen
Tiffani Thiessen is an American actress best known for her roles on the television series "Saved by the Bell" and "Beverly Hills, 90210."
-
E.
Heather Lind
Heather Lind is an American actress known for her work in film, television, and theater, including roles in projects like the indie comedy-drama "Mistress America."
- 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4887f7d048190b6d813b9f0fab3e7 |
completed | April 19, 2026, 7:47 a.m. |
Created at: April 10, 2026, 10:14 a.m.