Triple
T8224478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agia Marina |
E192142
|
entity |
| Predicate | distanceToTempleOfAphaia |
P81721
|
FINISHED |
| Object | a few kilometers |
—
|
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: a few kilometers | Statement: [Agia Marina, distanceToTempleOfAphaia, a few kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToTempleOfAphaia Context triple: [Agia Marina, distanceToTempleOfAphaia, a few kilometers]
-
A.
distanceFromMiletus
Indicates the spatial distance between a given place or object and the location of Miletus.
-
B.
approxDistanceToMytilene
Indicates that one entity is located at an approximate distance from the place or reference point named Mytilene.
-
C.
distanceFromSamosTownKilometers
Indicates the distance, measured in kilometers, between a given place and Samos Town.
-
D.
distanceFromHeraklion
Indicates the spatial distance between a given location and the city of Heraklion.
-
E.
distanceToPatras
Indicates the spatial distance between a given entity or location and the city of Patras.
- 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_69ca82c9a8ac81908b011c38698456e4 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb77cc351481908d7dcd6d3d15d59f |
completed | March 31, 2026, 7:29 a.m. |
| PD | Predicate disambiguation | batch_69cb36af41e081909dee92b9bc4947f1 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb4d9b03108190a6785834f21ff75e |
completed | March 31, 2026, 4:29 a.m. |
Created at: March 30, 2026, 5:45 p.m.