Triple
T5964844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kilkis |
E132725
|
entity |
| Predicate | distanceFrom Thessaloniki |
P56484
|
FINISHED |
| Object | about 50 kilometers north |
—
|
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 50 kilometers north | Statement: [Kilkis, distanceFrom Thessaloniki, about 50 kilometers north]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFrom Thessaloniki Context triple: [Kilkis, distanceFrom Thessaloniki, about 50 kilometers north]
-
A.
distanceFromThessaloniki
chosen
Indicates the spatial distance between a given entity and the city of Thessaloniki.
-
B.
distanceToThessaloniki
Indicates the spatial distance between a given entity’s location and the city of Thessaloniki.
-
C.
distanceFromKaterini_km
Indicates the distance, measured in kilometers, between a given place and Katerini.
-
D.
distanceFromHeraklion
Indicates the spatial distance between a given location and the city of Heraklion.
-
E.
distanceToTirana_km
Indicates the physical distance, measured in kilometers, between a given place and the city of Tirana.
- 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_69c0086c2364819091e9fe2f58fa2517 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03fb7f8a88190a8bd45208bda4a03 |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0335a635881909c58c1ef0f97f1e8 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4:03 p.m.