Triple
T988662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munich |
E21335
|
entity |
| Predicate | distanceToAlps |
P22793
|
FINISHED |
| Object | near the northern edge of the Alps |
—
|
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: near the northern edge of the Alps | Statement: [Munich, distanceToAlps, near the northern edge of the Alps]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAlps Context triple: [Munich, distanceToAlps, near the northern edge of the Alps]
-
A.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
B.
distanceToGeneva
Indicates the spatial distance between a given entity and the location of Geneva.
-
C.
relativeToMountEverest
Indicates a relationship that compares or situates something in reference to Mount Everest, such as in terms of location, height, scale, or significance.
-
D.
hasSkiResortNearby
Indicates that one location is situated close enough to another location that it can be considered to have a ski resort in its vicinity.
-
E.
flightDistance
Indicates the measured distance covered by a flight between its origin and destination.
- 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_69a493c383dc8190a03257f22d4b4183 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4a89a58819081a24b5b0a12f122 |
completed | March 1, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69a4b2abccbc8190a83af432f89eacf5 |
completed | March 1, 2026, 9:42 p.m. |
| PDg | Predicate description generation | batch_69a4b38630848190bd3898a4f42018ad |
completed | March 1, 2026, 9:45 p.m. |
Created at: March 1, 2026, 7:41 p.m.