Triple
T14363076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hall Pass |
E356152
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Jenna Fischer |
E223036
|
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: Jenna Fischer | Statement: [Hall Pass, starring, Jenna Fischer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jenna Fischer Context triple: [Hall Pass, starring, Jenna Fischer]
-
A.
Jenna Fischer
chosen
Jenna Fischer is an American actress best known for her role as Pam Beesly on the hit sitcom "The Office."
-
B.
Kristin Davis
Kristin Davis is an American actress best known for her role as Charlotte York on the television series "Sex and the City" and its related films.
-
C.
Megan Mullally
Megan Mullally is an American actress, comedian, and singer best known for her Emmy-winning role as Karen Walker on the television sitcom "Will & Grace."
-
D.
Cobie Smulders
Cobie Smulders is a Canadian actress best known for her role as Robin Scherbatsky on the sitcom "How I Met Your Mother" and for portraying Maria Hill in the Marvel Cinematic Universe.
-
E.
Rashida Jones
Rashida Jones is an American actress, writer, and producer known for roles in television series like "Parks and Recreation" and films such as "The Social Network," as well as her work behind the scenes as a screenwriter and filmmaker.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fabec088190bd8128371b29e958 |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c4cb0c4819094d59b4b1d43588b |
completed | May 8, 2026, 2:37 a.m. |
Created at: April 10, 2026, 1:15 a.m.