Triple
T246934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Indian rupee |
E5057
|
entity |
| Predicate | languagePanelCount |
P8721
|
FINISHED |
| Object | 15 Indian languages on language panel |
—
|
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: 15 Indian languages on language panel | Statement: [Indian rupee, languagePanelCount, 15 Indian languages on language panel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languagePanelCount Context triple: [Indian rupee, languagePanelCount, 15 Indian languages on language panel]
-
A.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
B.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
C.
languageOfInterface
Indicates the language used by or presented in a user interface.
-
D.
hasNumberOfScreens
Indicates the quantity of screens associated with or contained in a given entity.
-
E.
numberOfSpans
Indicates the total count of distinct spans or segments associated with an entity or within a specified context.
- 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d13b8088190a3f48f0388d57496 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b63b0bc8190864d7324d339fb48 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25c2ca46c81908c61696f31e59a98 |
completed | Feb. 28, 2026, 3:08 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.