Triple
T1734509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lashkari Zaban |
E37890
|
entity |
| Predicate | ISO639_1CodeOfReferredLanguage |
P5196
|
FINISHED |
| Object | ur |
—
|
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: ur | Statement: [Lashkari Zaban, ISO639_1CodeOfReferredLanguage, ur]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ISO639_1CodeOfReferredLanguage Context triple: [Lashkari Zaban, ISO639_1CodeOfReferredLanguage, ur]
-
A.
languageCodeISO639-2
Indicates that an entity is associated with a language identified by its ISO 639-2 three-letter code.
-
B.
languageCodeISO639-1
chosen
Indicates that the subject entity is associated with the specified two-letter ISO 639-1 language code.
-
C.
sharesISO639-3CodeWith
Indicates that two language entities share the same ISO 639-3 code, meaning they are treated as the same language in that coding system.
-
D.
ISO639Macrolanguage
Indicates that a language variety is part of a broader ISO 639-defined macrolanguage grouping that encompasses multiple closely related individual languages.
-
E.
ISO639Scope
Indicates the classification of a language according to its scope, such as whether it represents an individual language, a macrolanguage, or a collection of languages.
- 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_69a8861cc6ac8190ac0b2e31ccf62851 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab5c553e508190b0f511b05e07fa20 |
completed | March 6, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69aa61c25a648190892de94c997fb983 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.