Triple
T3456107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clásica de San Sebastián |
E72907
|
entity |
| Predicate | typicalDistance |
P18065
|
FINISHED |
| Object | around 220 km |
—
|
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: around 220 km | Statement: [Clásica de San Sebastián, typicalDistance, around 220 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalDistance Context triple: [Clásica de San Sebastián, typicalDistance, around 220 km]
-
A.
typicalLength
chosen
Indicates the usual or characteristic length associated with an entity or phenomenon.
-
B.
distanceCharacteristic
Indicates a relationship where an entity is described or constrained by some property or measure of distance (e.g., range, spacing, or separation).
-
C.
distanceCategory
Indicates the qualitative classification of how far apart two entities are from each other (e.g., near, medium, far).
-
D.
distance
Indicates the spatial separation or length between two points, objects, or locations.
-
E.
hasApproximateDrivingDistanceFrom
Indicates that one entity is located at an estimated or approximate driving distance from another entity, typically measured along road routes rather than as a precise or exact value.
- 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_69ad85b12a908190a1d10a6b03b4f8ae |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbaa77b9c81909376a5995cdaf6ac |
completed | March 8, 2026, 6:06 p.m. |
| PD | Predicate disambiguation | batch_69adae041d588190a84a02bca94adec8 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:16 p.m.