Triple
T15092082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skaraborg County |
E360443
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Falköping |
E526668
|
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: Falköping | Statement: [Skaraborg County, contains, Falköping]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Falköping Context triple: [Skaraborg County, contains, Falköping]
-
A.
Falköping
chosen
Falköping is a small Swedish town known for its surrounding ancient burial mounds, rolling agricultural landscape, and location between the plateaus of Mösseberg and Ålleberg.
-
B.
Falkenberg
Falkenberg is a coastal town in southwestern Sweden known for its beaches, fishing heritage, and location along the River Ätran.
-
C.
Falkenberg
Falkenberg is a locality in the borough of Lichtenberg in Berlin, Germany, known for its more rural character on the city's northeastern edge.
-
D.
Falkenberg
Falkenberg is a small municipality in the Rottal-Inn district of Lower Bavaria in southeastern Germany.
-
E.
Fagersta
Fagersta is an industrial town in central Sweden known for its steel production and manufacturing heritage.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0027925788190b955fdc6626adf7d |
completed | April 15, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff28b707481908611ae8e23de3294 |
completed | May 10, 2026, 2:50 a.m. |
Created at: April 10, 2026, 3:04 a.m.