Triple
T7539578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samos International Airport |
E178238
|
entity |
| Predicate | distanceFromSamosTownKilometers |
P77706
|
FINISHED |
| Object | approximately 14 |
—
|
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 14 | Statement: [Samos International Airport, distanceFromSamosTownKilometers, approximately 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromSamosTownKilometers Context triple: [Samos International Airport, distanceFromSamosTownKilometers, approximately 14]
-
A.
distanceFromHeraklion
Indicates the spatial distance between a given location and the city of Heraklion.
-
B.
approxDistanceToMytilene
Indicates that one entity is located at an approximate distance from the place or reference point named Mytilene.
-
C.
distanceFromMiletus
Indicates the spatial distance between a given place or object and the location of Miletus.
-
D.
distanceFromKaterini_km
Indicates the distance, measured in kilometers, between a given place and Katerini.
-
E.
distanceFromThessaloniki
Indicates the spatial distance between a given entity and the city of Thessaloniki.
- 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_69c69f2be3888190a6667a27f8f195e9 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f87280f0819097d4ffc2e1b62cf8 |
completed | March 27, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d8eedc81908c1ae421e0e63798 |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6f5cea2748190afd607ef93e8d66c |
completed | March 27, 2026, 9:25 p.m. |
Created at: March 27, 2026, 3:48 p.m.