Triple
T8639849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer Garner |
E204617
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jennifer Garner |
E36244
|
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: Jennifer Garner | Statement: [Jennifer Garner, name, Jennifer Garner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennifer Garner Context triple: [Jennifer Garner, name, Jennifer Garner]
-
A.
Jennifer Garner
chosen
Jennifer Garner is an American actress known for her starring role in the television series "Alias" and for her performances in films such as "13 Going on 30" and "Dallas Buyers Club."
-
B.
Jessica Alba
Jessica Alba is an American actress and businesswoman known for her roles in films like "Fantastic Four" and for founding the consumer goods company The Honest Company.
-
C.
Kate Bosworth
Kate Bosworth is an American actress best known for her roles in films such as "Blue Crush" and "Superman Returns."
-
D.
Katherine Heigl
Katherine Heigl is an American actress and former fashion model best known for her roles in the television series "Grey's Anatomy" and various romantic comedy films.
-
E.
Rachel McAdams
Rachel McAdams is a Canadian actress known for her versatile performances in films such as "Mean Girls," "The Notebook," and "Spotlight."
- 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_69ca834ca1c88190a11ffb0200342fac |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc47650a14819094855aa8d062ebbc |
completed | March 31, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdb759f6c819085939e38e0281361 |
completed | April 3, 2026, 3:23 p.m. |
Created at: March 30, 2026, 6:28 p.m.