Triple
T1816386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schiphol railway station |
E40444
|
entity |
| Predicate | distanceToAmsterdamCentraal |
P32616
|
FINISHED |
| Object | approximately 17 kilometres |
—
|
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: approximately 17 kilometres | Statement: [Schiphol railway station, distanceToAmsterdamCentraal, approximately 17 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAmsterdamCentraal Context triple: [Schiphol railway station, distanceToAmsterdamCentraal, approximately 17 kilometres]
-
A.
cityDistanceFromBrussels_km
Indicates the distance, measured in kilometers, between a given city and Brussels.
-
B.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
C.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
D.
distanceToUppsalaByRail
Indicates the measured distance between a given place and Uppsala when traveling by railway.
-
E.
distanceToHelsinki_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Helsinki.
- 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_69a8864526c081908a3a4d74f689e2c5 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba67721788190951beae25e885457 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61d884548190a19cf3a6b5ae9d48 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69aba67554788190b429f2b9f0a70310 |
completed | March 7, 2026, 4:15 a.m. |
Created at: March 4, 2026, 7:32 p.m.