Triple
T9854825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weeds |
E239556
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Zooey Deschanel |
E148184
|
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: Zooey Deschanel | Statement: [Weeds, starring, Zooey Deschanel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zooey Deschanel Context triple: [Weeds, starring, Zooey Deschanel]
-
A.
Zooey Deschanel
chosen
Zooey Deschanel is an American actress, singer, and songwriter known for her quirky, offbeat roles in films like "500 Days of Summer" and the TV series "New Girl."
-
B.
Amanda Peet
Amanda Peet is an American actress known for her work in films like "The Whole Nine Yards" and television series such as "Studio 60 on the Sunset Strip" and "Togetherness."
-
C.
Kaley Cuoco
Kaley Cuoco is an American actress best known for her comedic television roles, particularly as Penny on the hit sitcom "The Big Bang Theory."
-
D.
Anna Kendrick
Anna Kendrick is an American actress and singer known for her versatile performances in films such as "Pitch Perfect," "Up in the Air," and the musical fantasy "Into the Woods."
-
E.
Amanda Seyfried
Amanda Seyfried is an American actress and singer known for her roles in films such as "Mamma Mia!", "Les Misérables," and "Mean Girls."
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3960fb481909c90d6d6cafc6222 |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23cfc50948190aae82dced585fa29 |
completed | April 5, 2026, 10:44 a.m. |
Created at: March 30, 2026, 8:34 p.m.