Triple
T5357572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winschoten |
E102734
|
entity |
| Predicate | distanceToGroningenCity_km |
P63224
|
FINISHED |
| Object | about 30 |
—
|
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: about 30 | Statement: [Winschoten, distanceToGroningenCity_km, about 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGroningenCity_km Context triple: [Winschoten, distanceToGroningenCity_km, about 30]
-
A.
distanceToAmsterdamCentraal
Indicates the physical distance between a given location and Amsterdam Centraal station.
-
B.
roadDistanceToUtrecht_km
Indicates the distance in kilometers between an entity and Utrecht when traveling by road.
-
C.
distanceFromCopenhagen
Indicates the spatial distance between a given entity and the location of Copenhagen.
-
D.
distanceFromUppsala_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Uppsala.
-
E.
cityDistanceFromBrussels_km
Indicates the distance, measured in kilometers, between a given city and Brussels.
- 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_69bd43d8f7248190b64c140734b5c9a8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd863099b081909d20f7014b98de5a |
completed | March 20, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69bd845c6f108190832a8d14b356368a |
completed | March 20, 2026, 5:31 p.m. |
| PDg | Predicate description generation | batch_69bd85e69e808190b29548670fd2900a |
completed | March 20, 2026, 5:37 p.m. |
Created at: March 20, 2026, 2:01 p.m.