Triple
T11584863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanna Alström |
E274723
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Alström |
E274723
|
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: Alström | Statement: [Hanna Alström, familyName, Alström]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alström Context triple: [Hanna Alström, familyName, Alström]
-
A.
Alström
chosen
Alström is a Swedish surname borne by individuals such as actress Hanna Alström.
-
B.
Sundström
Sundström is a Swedish surname borne by various notable individuals, including actress Rebecca Ferguson.
-
C.
Lindberg
Lindberg is a small municipality in the Regen district of Bavaria, Germany, known for its location in the Bavarian Forest region.
-
D.
Edström
Edström is a Swedish surname borne by several notable figures, including athletes, politicians, and industrial leaders.
-
E.
Andermann
Andermann is a German-language surname borne by individuals such as Hermine Andermann.
- 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d89462203881908870e991a5b21770 |
completed | April 10, 2026, 6:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e7142d442c8190a48372e0e17db517 |
completed | April 21, 2026, 6:07 a.m. |
Created at: April 8, 2026, 9:38 p.m.