Triple
T18249461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kato Nevrokopi |
E437045
|
entity |
| Predicate | distanceFromDrama |
P7750
|
FINISHED |
| Object | about 42 kilometers northwest |
—
|
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 42 kilometers northwest | Statement: [Kato Nevrokopi, distanceFromDrama, about 42 kilometers northwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromDrama Context triple: [Kato Nevrokopi, distanceFromDrama, about 42 kilometers northwest]
-
A.
distanceFromTheater
Indicates the measured spatial distance between a given entity and a specified theater.
-
B.
distancedFrom
Indicates that one entity is physically or metaphorically kept at a certain distance or separation from another entity.
-
C.
distance
chosen
Indicates the spatial separation or length between two points, objects, or locations.
-
D.
distanceToHollywood
Indicates the spatial distance between a given entity and Hollywood.
-
E.
distanceCategory
Indicates the qualitative classification of how far apart two entities are from each other (e.g., near, medium, far).
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4fd7fa3708190baefd8d938d20807 |
completed | April 19, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69e44fcdee748190bae6fb76e0cb22f3 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:33 a.m.