Triple
T17093680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al Boraq high-speed rail |
E414785
|
entity |
| Predicate | travelTimeTangierRabatAfter |
P125884
|
FINISHED |
| Object | about 1 hour 20 minutes |
—
|
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 1 hour 20 minutes | Statement: [Al Boraq high-speed rail, travelTimeTangierRabatAfter, about 1 hour 20 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelTimeTangierRabatAfter Context triple: [Al Boraq high-speed rail, travelTimeTangierRabatAfter, about 1 hour 20 minutes]
-
A.
distanceFromMarrakesh
Indicates the spatial distance between a given location and the city of Marrakesh.
-
B.
travelTimeMeccaMedina
Indicates the duration or time required to travel between Mecca and Medina.
-
C.
distanceToAgadir
Indicates the spatial distance between a given entity’s location and the city of Agadir.
-
D.
travelTimeToParisByRER
Indicates the duration required to travel to Paris using the RER train network.
-
E.
distanceToTunis
Indicates the spatial distance between a given entity’s location and the city of Tunis.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbfb89348190942984037bd3bd2e |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d67b14481909fcdbdeaa5c34785 |
completed | April 18, 2026, 10:31 a.m. |
| PDg | Predicate description generation | batch_69e37542d060819082aa73948eb8ebd4 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:35 a.m.