Triple
T22634241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monastir Habib Bourguiba International Airport |
E558635
|
entity |
| Predicate | distanceToSousse |
P149011
|
FINISHED |
| Object | approximately 20 km from Sousse |
—
|
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 20 km from Sousse | Statement: [Monastir Habib Bourguiba International Airport, distanceToSousse, approximately 20 km from Sousse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSousse Context triple: [Monastir Habib Bourguiba International Airport, distanceToSousse, approximately 20 km from Sousse]
-
A.
distanceToTunis
Indicates the spatial distance between a given entity’s location and the city of Tunis.
-
B.
distanceToBizerte
Indicates the measured distance between a given entity’s location and the city of Bizerte.
-
C.
distanceToOuarzazate
Indicates the measured or estimated spatial distance between a given entity and the location of Ouarzazate.
-
D.
distanceFromSanaa
Indicates the spatial distance between an entity and the location of Sanaa.
-
E.
distanceFromMarrakesh
Indicates the spatial distance between a given location and the city of Marrakesh.
- 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_69e245467d9881908d6985bd0db7a1f1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1700be10c8190830393fdbec1033d |
completed | April 29, 2026, 2:42 a.m. |
| PD | Predicate disambiguation | batch_69ee62855558819080da946c7b35a160 |
completed | April 26, 2026, 7:07 p.m. |
| PDg | Predicate description generation | batch_69ee8841e9cc81908d23b34215e3be71 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 3:03 p.m.