Triple
T6628179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The O.C. |
E149854
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Rachel Bilson |
E243720
|
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: Rachel Bilson | Statement: [The O.C., portrayedBy, Rachel Bilson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Bilson Context triple: [The O.C., portrayedBy, Rachel Bilson]
-
A.
Rachel Bilson
chosen
Rachel Bilson is an American actress best known for her television roles, including starring in the series "The O.C." and other popular TV dramas and comedies.
-
B.
Laura Prepon
Laura Prepon is an American actress best known for her roles on the television series That '70s Show and Orange Is the New Black.
-
C.
Danielle Panabaker
Danielle Panabaker is an American actress best known for her role as Caitlin Snow/Killer Frost in the Arrowverse television series "The Flash."
-
D.
Danielle Fishel
Danielle Fishel is an American actress and television personality best known for playing Topanga Lawrence on the sitcom "Boy Meets World" and its sequel "Girl Meets World."
-
E.
Jennifer Love Hewitt
Jennifer Love Hewitt is an American actress and singer best known for her roles in 1990s and 2000s film and television, including the horror and teen drama genres.
- 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_69c687ee50048190aa151765bef16193 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6afa2e4a48190ba3c70013bab14f2 |
completed | March 27, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70ae8248081909a0f6628583f4626 |
completed | March 27, 2026, 10:55 p.m. |
Created at: March 27, 2026, 1:59 p.m.