Triple
T12683531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hesher |
E303006
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Natalie Portman |
E49597
|
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: Natalie Portman | Statement: [Hesher, starring, Natalie Portman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Natalie Portman Context triple: [Hesher, starring, Natalie Portman]
-
A.
Natalie Portman
chosen
Natalie Portman is an acclaimed Israeli-American actress known for her versatile film roles and her Academy Award-winning performance in "Black Swan."
-
B.
Mia Wasikowska
Mia Wasikowska is an Australian actress known for her versatile performances in films such as "Alice in Wonderland," "Jane Eyre," and various independent dramas.
-
C.
Kate Bosworth
Kate Bosworth is an American actress best known for her roles in films such as "Blue Crush" and "Superman Returns."
-
D.
Maggie Gyllenhaal
Maggie Gyllenhaal is an American actress known for her nuanced performances in independent films and major studio productions, including her acclaimed role as Rachel Dawes in Christopher Nolan’s Batman franchise.
-
E.
Arielle Kebbel
Arielle Kebbel is an American actress and former model known for her roles in film and television series such as "The Vampire Diaries," "Gilmore Girls," and various romantic comedies and dramas.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961d68358819095bdaab8adf1dcf0 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671a733a48190b55d296573c86eaf |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:21 p.m.