Triple
T18037257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Starodub |
E431540
|
entity |
| Predicate | distanceToBryansk |
P129537
|
FINISHED |
| Object | approximately 170 km southwest |
—
|
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: approximately 170 km southwest | Statement: [Starodub, distanceToBryansk, approximately 170 km southwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBryansk Context triple: [Starodub, distanceToBryansk, approximately 170 km southwest]
-
A.
distanceToVitebsk_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Vitebsk.
-
B.
distanceToSmolensk
Indicates the spatial distance between a given entity and the location of Smolensk.
-
C.
distanceToKostroma
Indicates the spatial distance between a given entity and the location of Kostroma.
-
D.
distanceToNizhnyNovgorod
Indicates the spatial distance between a given entity and the location of Nizhny Novgorod.
-
E.
distanceFromMoscow_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and Moscow.
- 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.