Triple
T4658556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abasha |
E102469
|
entity |
| Predicate | roadDistanceToTbilisi |
P41077
|
FINISHED |
| Object | approximately 280 km |
—
|
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 280 km | Statement: [Abasha, roadDistanceToTbilisi, approximately 280 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadDistanceToTbilisi Context triple: [Abasha, roadDistanceToTbilisi, approximately 280 km]
-
A.
distanceFrom Tbilisi
chosen
Indicates the spatial distance between a given location or entity and the city of Tbilisi.
-
B.
distanceFromKutaisi
Indicates the spatial distance measured from the location of Kutaisi to another specified place or entity.
-
C.
distanceToKutaisi_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Kutaisi.
-
D.
distanceFromYerevan_km
Indicates the physical distance, measured in kilometers, between a given place and the city of Yerevan.
-
E.
distanceFromAshgabat
Indicates the measured spatial distance between a given location and the city of Ashgabat.
- 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd63271a548190bd9662b69a45d9a5 |
completed | March 20, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69bd62126b0c81909ba3f21b21e30d54 |
completed | March 20, 2026, 3:04 p.m. |
Created at: March 20, 2026, 1:15 p.m.