Triple
T7531774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Am Greta |
E178039
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Hanna Lejonqvist |
E22539
|
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: Hanna Lejonqvist | Statement: [I Am Greta, editedBy, Hanna Lejonqvist]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanna Lejonqvist Context triple: [I Am Greta, editedBy, Hanna Lejonqvist]
-
A.
Hanna Lejonqvist
chosen
Hanna Lejonqvist is a film editor known for her work on the documentary "I Am Greta," which follows climate activist Greta Thunberg.
-
B.
Hanna Alström
Hanna Alström is a Swedish actress best known internationally for her role as Princess Tilde in the action-comedy film "Kingsman: The Secret Service" and its sequel.
-
C.
Therese Andersson
Therese Andersson is known as the wife of former Swedish NHL star goaltender Henrik Lundqvist.
-
D.
Katarina Frostenson
Katarina Frostenson is a Swedish poet, writer, and former member of the Swedish Academy known for her influential and experimental contributions to contemporary Swedish literature.
-
E.
Åsa Larsson
Åsa Larsson is a Swedish crime fiction author best known for her Rebecka Martinsson series set in northern Sweden.
- 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_69c69f2acdbc8190b5a8320168c1d0ba |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f84753fc81908bee2013004ef5fb |
completed | March 27, 2026, 9:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c84640cd2c819094d8f72d82c71e67 |
completed | March 28, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:47 p.m.