Triple
T15197465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Älvsborg County |
E363173
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Alingsås |
E363745
|
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: Alingsås | Statement: [Älvsborg County, containsCity, Alingsås]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alingsås Context triple: [Älvsborg County, containsCity, Alingsås]
-
A.
Alingsås
chosen
Alingsås is a Swedish town known for its historic wooden architecture, café culture, and annual Lights in Alingsås illumination festival.
-
B.
Häggenås
Häggenås is a small locality in Jämtland County, northern Sweden, situated within Östersund Municipality.
-
C.
Södertälje
Södertälje is a Swedish city southwest of Stockholm known for its industrial heritage, diverse population, and strategic location linking Lake Mälaren with the Baltic Sea via the Södertälje Canal.
-
D.
Eskilstuna
Eskilstuna is an industrial city in central Sweden known historically for its metalworking and engineering industries.
-
E.
Enköping
Enköping is a small Swedish town known for its numerous themed parks and gardens, often called “Sweden’s nearest town” due to its central location relative to several major cities.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0067fcc788190abdc083d4eadeb36 |
completed | April 15, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed3342624819087be35acadd88136 |
completed | May 9, 2026, 6:24 a.m. |
Created at: April 10, 2026, 3:10 a.m.