Triple
T16781250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amy Acker |
E407861
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Amy Acker |
E407861
|
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: Amy Acker | Statement: [Amy Acker, name, Amy Acker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amy Acker Context triple: [Amy Acker, name, Amy Acker]
-
A.
Amy Acker
chosen
Amy Acker is an American actress best known for her roles in television series such as "Angel," "Person of Interest," and "Dollhouse."
-
B.
Allison Mack
Allison Mack is an American actress best known for her role as Chloe Sullivan on the television series "Smallville."
-
C.
Gina Torres
Gina Torres is an American actress known for her roles in television series such as "Suits," "Firefly," and "Hannibal."
-
D.
Melissa Cobb
Melissa Cobb is an American film producer best known for her work on major animated features, including the Kung Fu Panda franchise.
-
E.
AnnaLynne McCord
AnnaLynne McCord is an American actress and activist best known for her roles in television series like "90210" and "Nip/Tuck" and in various horror and thriller films.
- 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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b216726881908ddc9cdc772cd5e4 |
completed | April 18, 2026, 4:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d45013048190a8073f34820ca85a |
completed | May 10, 2026, 6:54 p.m. |
Created at: April 10, 2026, 5:22 a.m.