Triple
T8238504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fredrik Reinfeldt |
E192470
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Täby |
E20860
|
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: Täby | Statement: [Fredrik Reinfeldt, birthPlace, Täby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Täby Context triple: [Fredrik Reinfeldt, birthPlace, Täby]
-
A.
Täby, Sweden
Täby is a suburban municipality and town north of Stockholm, Sweden, known for its affluent residential areas, historical runestones, and modern shopping and business centers.
-
B.
Täby Municipality
chosen
Täby Municipality is a suburban local government area north of central Stockholm, Sweden, known for its affluent residential neighborhoods and strong commuter links to the capital.
-
C.
Ronneby
Ronneby is a historic town in southern Sweden known for its well-preserved wooden architecture, spa traditions, and scenic location in Blekinge County.
-
D.
Strängnäs
Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
-
E.
Tärnsjö
Tärnsjö is a small locality in central Sweden known for its rural setting and traditional leather tanning industry.
- 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_69ca82dc8f148190a2c75a98501a7b91 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb783a8cf48190bf85394fd3bd79e2 |
completed | March 31, 2026, 7:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd3504e6ac8190b4cb12c80a7e7fc0 |
completed | April 1, 2026, 3:08 p.m. |
Created at: March 30, 2026, 5:47 p.m.