Triple
T1309966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shahmukhi script |
E27965
|
entity |
| Predicate | digitalUsage |
P1446
|
FINISHED |
| Object | used on websites for Punjabi in Pakistan |
—
|
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: used on websites for Punjabi in Pakistan | Statement: [Shahmukhi script, digitalUsage, used on websites for Punjabi in Pakistan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: digitalUsage Context triple: [Shahmukhi script, digitalUsage, used on websites for Punjabi in Pakistan]
-
A.
dataUse
Indicates how data is intended to be accessed, processed, or applied within a particular context or activity.
-
B.
fanUsage
Indicates that an entity uses, operates, or relies on a fan (e.g., for cooling, ventilation, or air circulation) in relation to another entity or context.
-
C.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
D.
usedInDigitalCommunication
chosen
Indicates that something is employed as a medium, tool, or element within digital or electronic communication processes.
-
E.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
- 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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c15490a88190872c3d2698a8f9c9 |
completed | March 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69a4bee9e4a88190b22ab2ee831a23c9 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.