Triple
T5394322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tumba |
E120611
|
entity |
| Predicate | hasHistoricalSite |
P1098
|
FINISHED |
| Object | Tumba Bruk |
E120611
|
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: Tumba Bruk | Statement: [Tumba, hasHistoricalSite, Tumba Bruk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tumba Bruk Context triple: [Tumba, hasHistoricalSite, Tumba Bruk]
-
A.
Tumba
chosen
Tumba is a suburban locality in Stockholm County, Sweden, known for its residential areas and historical paper mill industry.
-
B.
Toten
Toten is a traditional rural district in eastern Norway known for its agriculture and scenic landscape, located within Innlandet county.
-
C.
Den Osse
Den Osse is a small Dutch coastal village and marina resort on the Grevelingenmeer, known for water sports, diving, and recreational tourism.
-
D.
Gravedona
Gravedona is a picturesque town on the shores of Lake Como in northern Italy, known for its historic churches and scenic lakeside setting.
-
E.
Vestre Toten
Vestre Toten is a municipality in Innlandet county, Norway, known for its mix of rural landscapes, small towns, and industrial activity.
- 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_69bd4637b92c8190b815b6443ae4b323 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd87441a208190b79561614759894b |
completed | March 20, 2026, 5:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf336d43a081909ab05d237c297c7b |
completed | March 22, 2026, 12:10 a.m. |
Created at: March 20, 2026, 2:04 p.m.