Triple
T10323653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lina Leandersson as Eli |
E242701
|
entity |
| Predicate | residesAtBeginningOfFilm |
P47721
|
FINISHED |
| Object | apartment near Oskar’s building |
—
|
LITERAL 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: apartment near Oskar’s building | Statement: [Lina Leandersson as Eli, residesAtBeginningOfFilm, apartment near Oskar’s building]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: residesAtBeginningOfFilm Context triple: [Lina Leandersson as Eli, residesAtBeginningOfFilm, apartment near Oskar’s building]
-
A.
beginsWithFilm
Indicates that one entity (typically a series, collection, or sequence) starts or is initiated with a particular film as its first element.
-
B.
precedesInFilm
Indicates that one film is released or occurs earlier in sequence or narrative order than another film.
-
C.
residenceAtStartOfFilm
chosen
Indicates the place where a person or character is living at the beginning of the film.
-
D.
firstAppearanceFilm
Indicates the film in which an entity (such as a character or person) makes its first on-screen appearance.
-
E.
basedInFilm
Indicates that something (such as a character, event, or work) is situated, set, or primarily located within the context or universe of a particular film.
- F. None of above.
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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ccb7ec8190a538cf279e48116e |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f64a648190a79980d647898eb0 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:50 a.m.