Triple
T37483366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | President of Pakistan Muslim League (N) |
E931464
|
entity |
| Predicate | equivalentTitleInUrdu |
P183014
|
FINISHED |
| Object | صدر پاکستان مسلم لیگ (ن) |
—
|
NE NERFINISHED |
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: صدر پاکستان مسلم لیگ (ن) | Statement: [President of Pakistan Muslim League (N), equivalentTitleInUrdu, صدر پاکستان مسلم لیگ (ن)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: equivalentTitleInUrdu Context triple: [President of Pakistan Muslim League (N), equivalentTitleInUrdu, صدر پاکستان مسلم لیگ (ن)]
-
A.
equivalentTitleInLanguage
chosen
Indicates that two titles are equivalent in meaning or reference, but expressed in a specified language.
-
B.
equivalentTitleInPolish
Indicates that one entity has a title in Polish that is equivalent in meaning or status to the title of another entity.
-
C.
equivalentTitleInSwedish
Indicates that one entity has a title in Swedish that is equivalent in meaning or status to the title of another entity.
-
D.
equivalentTitleInItalian
Indicates that one entity’s title is an equivalent version of another entity’s title expressed in Italian.
-
E.
equivalentTitleInKorean
Indicates that one title has an equivalent or corresponding title expressed in the Korean language.
- 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_69fba68077788190b311e027435fcf87 |
completed | May 6, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fba34c65ac8190b298f0f00d1dcc0e |
completed | May 6, 2026, 8:23 p.m. |
Created at: May 3, 2026, 4:17 p.m.