Triple
T4396895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ushguli |
E99512
|
entity |
| Predicate | numberOfTowersApproximate |
P5319
|
FINISHED |
| Object | more than 30 Svan towers |
—
|
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: more than 30 Svan towers | Statement: [Ushguli, numberOfTowersApproximate, more than 30 Svan towers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTowersApproximate Context triple: [Ushguli, numberOfTowersApproximate, more than 30 Svan towers]
-
A.
numberOfTowers
chosen
Indicates the quantity of towers associated with or contained by a given entity.
-
B.
towerCount
Indicates the number of towers associated with or present in a given entity or context.
-
C.
numberOfBuildings
Indicates the total count of buildings associated with a given entity or within a specified context.
-
D.
numberOfTunnels
Indicates the quantity of tunnels associated with or passing through a given entity or location.
-
E.
hasTowerHeight
Indicates that an entity (such as a tower or structure) has a specific height value associated with it.
- 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_69b345506b408190b0e3dee616738a7d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352aca86c8190b5af7e6600072066 |
completed | March 12, 2026, 11:56 p.m. |
| PD | Predicate disambiguation | batch_69b34f597998819092477efdedb51427 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:20 p.m.