Triple
T1700402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aegina |
E36754
|
entity |
| Predicate | distanceFromAthens |
P22795
|
FINISHED |
| Object | about 27 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: about 27 kilometers | Statement: [Aegina, distanceFromAthens, about 27 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromAthens Context triple: [Aegina, distanceFromAthens, about 27 kilometers]
-
A.
distanceFromAlexandria_km
Indicates the distance, measured in kilometers, between a given location and Alexandria.
-
B.
distanceToHelsinki_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Helsinki.
-
C.
distanceFromMediterranean
Indicates the measured spatial distance between a given location and the Mediterranean Sea.
-
D.
rankByAreaInGreece
Indicates the relative ordering of entities based on their area size within the geographic boundaries of Greece.
-
E.
approximateDistanceKm
chosen
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
- F. None of above.
Provenance (3 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_69a886163dec8190859c514232a37a05 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf169da888190b3aa334752f1952b |
completed | March 6, 2026, 3:23 p.m. |
| PD | Predicate disambiguation | batch_69aa61b8ce348190b46154af0b041ff0 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.