Triple
T26857674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiruna Mine |
E676238
|
entity |
| Predicate | cityRelocationImpact |
P133158
|
FINISHED |
| Object | contributed to relocation of Kiruna town center |
—
|
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: contributed to relocation of Kiruna town center | Statement: [Kiruna Mine, cityRelocationImpact, contributed to relocation of Kiruna town center]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityRelocationImpact Context triple: [Kiruna Mine, cityRelocationImpact, contributed to relocation of Kiruna town center]
-
A.
cityAfterRelocation
Indicates the city where an entity is located after it has been moved or relocated from a previous place.
-
B.
cityRelocationStatus
Indicates the status or stage of a city’s relocation process from one place to another.
-
C.
impactOnUrbanAreas
chosen
Indicates the effect or influence that something has on cities or densely populated urban environments.
-
D.
affectedCity
Indicates that a particular city is impacted or influenced by a specified event, action, or condition.
-
E.
cityRegulationImpact
Indicates how a specific city regulation affects or influences certain activities, entities, or conditions.
- 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_69eee9b9d7708190a15d7485709ae981 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61b98322881908adb98b258af26d5 |
completed | May 2, 2026, 3:43 p.m. |
| PD | Predicate disambiguation | batch_69f611ad2eb48190ac1ed0090f13f7a9 |
completed | May 2, 2026, 3:01 p.m. |
Created at: April 27, 2026, 5:22 a.m.