Triple
T37483365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | President of Pakistan Muslim League (N) |
E931464
|
entity |
| Predicate | languageOfEquivalentTitle |
P183014
|
FINISHED |
| Object | Urdu |
—
|
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: Urdu | Statement: [President of Pakistan Muslim League (N), languageOfEquivalentTitle, Urdu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfEquivalentTitle Context triple: [President of Pakistan Muslim League (N), languageOfEquivalentTitle, Urdu]
-
A.
equivalentTitleInLanguage
chosen
Indicates that two titles are equivalent in meaning or reference, but expressed in a specified language.
-
B.
languageOfAlternativeTitle
Indicates the language in which an alternative or variant title of an entity is expressed.
-
C.
titleInLanguage
Indicates that a specific title or name is expressed in a particular language.
-
D.
commonTitleLanguage
Indicates that two entities share the same language in which their titles are expressed.
-
E.
equivalentTitleInFrench
Indicates that one entity’s title is the equivalent or corresponding title of another entity, specifically expressed in French.
- 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_69f76ec382248190b47844df596123c6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a019579463c8190b51e39183b57bbaf |
completed | May 11, 2026, 8:38 a.m. |
| PD | Predicate disambiguation | batch_6a0192d309488190a3d86c93e7138c77 |
completed | May 11, 2026, 8:26 a.m. |
Created at: May 3, 2026, 4:17 p.m.