Triple
T4306948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tskaltubo |
E99976
|
entity |
| Predicate | distanceToBlackSea_km |
P48447
|
FINISHED |
| Object | approximately 70 |
—
|
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 70 | Statement: [Tskaltubo, distanceToBlackSea_km, approximately 70]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBlackSea_km Context triple: [Tskaltubo, distanceToBlackSea_km, approximately 70]
-
A.
distanceToBlackSea
chosen
Indicates the measured spatial distance between a given entity and the Black Sea.
-
B.
distanceFromMediterranean
Indicates the measured spatial distance between a given location and the Mediterranean Sea.
-
C.
distanceToRussianBorder_km
Indicates the physical distance, measured in kilometers, between a given location and the nearest point on the Russian border.
-
D.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
E.
distanceFrom Tbilisi
Indicates the spatial distance between a given location or entity and the city of Tbilisi.
- 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_69b345528ebc8190b5abc7e95094792d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350bb78cc8190a850aca47d8711cf |
completed | March 12, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69b347ff45cc8190b0cc335a94cc3d73 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:09 p.m.