Triple
T18839988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kongsberg Silver Mines |
E460765
|
entity |
| Predicate | hasTotalTunnelLength |
P114992
|
FINISHED |
| Object | over 300 kilometres |
—
|
LITERAL 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: over 300 kilometres | Statement: [Kongsberg Silver Mines, hasTotalTunnelLength, over 300 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTotalTunnelLength Context triple: [Kongsberg Silver Mines, hasTotalTunnelLength, over 300 kilometres]
-
A.
hasTunnelLengthApprox
Indicates that an entity has a tunnel whose length is approximately a specified value.
-
B.
lengthOfTunnels
chosen
Indicates the measured extent or distance of tunnels associated with an entity.
-
C.
numberOfTunnels
Indicates the quantity of tunnels associated with or passing through a given entity or location.
-
D.
tunnelCircumference
Indicates the circular distance around the inner boundary of a tunnel’s cross-section.
-
E.
hasTunnelSections
Indicates that an entity includes or is composed of multiple distinct tunnel segments or portions.
- F. None of above.
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_69d8dcfa11e4819090ab1ef5bdcd2b2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5b8e8f57081909edbbcaf56189816 |
completed | April 20, 2026, 5:26 a.m. |
| PD | Predicate disambiguation | batch_69e48d1e7dac81909ea1e758c87773c5 |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:56 a.m.