Triple
T36082304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Brandenburg |
E1043682
|
entity |
| Predicate | hasProximityEffect |
P204724
|
FINISHED |
| Object | commuter links to Berlin |
—
|
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: commuter links to Berlin | Statement: [Northern Brandenburg, hasProximityEffect, commuter links to Berlin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProximityEffect Context triple: [Northern Brandenburg, hasProximityEffect, commuter links to Berlin]
-
A.
hasNearbyMode
Indicates that one entity has another entity located close enough to be considered in its immediate vicinity or surrounding area.
-
B.
hasSoundProjection
Indicates that one entity emits, directs, or projects sound toward or into another entity or space.
-
C.
hasProximitySensor
Indicates that an entity is equipped with a sensor capable of detecting nearby objects or measuring its distance to them.
-
D.
hasGuitarEffect
Indicates that one entity applies, uses, or is associated with a particular guitar effect in relation to another entity.
-
E.
hasElectronicEffect
Indicates that one entity exerts or contributes an electronic influence or effect on another entity within a specified context.
- F. None of above. chosen
Provenance (4 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_69f76e3154908190a6f702671c2bea08 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0895b48190acdd88dc10db7be7 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:08 p.m.