Triple
T18037258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Starodub |
E431540
|
entity |
| Predicate | distanceToUkraineBorder |
P129538
|
FINISHED |
| Object | relatively close |
—
|
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: relatively close | Statement: [Starodub, distanceToUkraineBorder, relatively close]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToUkraineBorder Context triple: [Starodub, distanceToUkraineBorder, relatively close]
-
A.
distanceFromKyiv
Indicates the spatial distance between a given entity’s location and the city of Kyiv.
-
B.
distanceToRussianBorder_km
Indicates the physical distance, measured in kilometers, between a given location and the nearest point on the Russian border.
-
C.
distanceToDonetsk
Indicates the spatial distance between a given entity and the location of Donetsk.
-
D.
distanceToPolishBorder
Indicates the measured distance between a given location and the nearest point on the border of Poland.
-
E.
distanceToIvanoFrankivsk
Indicates the spatial distance between a given entity and the location of Ivano-Frankivsk.
- 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_69d8b9050fb48190890155145deb0a66 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4be3bc3208190a6db569e79f06232 |
completed | April 19, 2026, 11:36 a.m. |
| PD | Predicate disambiguation | batch_69e3f908da508190a088aa837ea5b7af |
completed | April 18, 2026, 9:35 p.m. |
| PDg | Predicate description generation | batch_69e42d8eefa88190a700c7c1b4213e46 |
completed | April 19, 2026, 1:19 a.m. |
Created at: April 10, 2026, 10:25 a.m.