Triple
T10323636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lina Leandersson as Eli |
E242701
|
entity |
| Predicate | closeTo |
P350
|
FINISHED |
| Object | Oskar |
E21155
|
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: Oskar | Statement: [Lina Leandersson as Eli, closeTo, Oskar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oskar Context triple: [Lina Leandersson as Eli, closeTo, Oskar]
-
A.
Oskar
chosen
Oskar is a masculine given name of Germanic origin, commonly used in various European countries.
-
B.
Erich
Erich is a masculine given name of German origin, commonly used in German-speaking countries and beyond.
-
C.
Helmut
Helmut is a masculine given name of German origin, historically common in German-speaking countries.
-
D.
Alois
Alois is a masculine given name of Germanic origin, notably borne by Alois Hitler, the father of Adolf Hitler.
-
E.
Hans
Hans is a masculine given name of Germanic origin commonly used in Germanic and Scandinavian countries.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d6ce683c8190bf5385dd04bf2de8 |
completed | April 7, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7fb69410c81909b33e44a04ab77d9 |
completed | April 9, 2026, 7:18 p.m. |
Created at: April 6, 2026, 11:50 a.m.