Triple
T28630410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkish Wikipedia |
E724624
|
entity |
| Predicate | countryWithLargestUserBase |
P47514
|
FINISHED |
| Object | Turkey |
—
|
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: Turkey | Statement: [Turkish Wikipedia, countryWithLargestUserBase, Turkey]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryWithLargestUserBase Context triple: [Turkish Wikipedia, countryWithLargestUserBase, Turkey]
-
A.
countryWithSignificantPopulation
Indicates that a country has a notably large or impactful number of people, relative to some defined threshold or comparison set.
-
B.
countryWithLargestNumberOfSpeakers
Indicates the country in which the highest number of speakers of a given language reside.
-
C.
countryWithLargeFollowing
Indicates that a country has a notably large number of supporters, fans, or followers relative to some context or comparison set.
-
D.
hasLargestCountryByPopulation
Indicates that, among a set of compared entities, the subject is associated with the country that has the highest population.
-
E.
countryWithLargestShare
chosen
Indicates that one entity is the country possessing the greatest proportion or share of a specified quantity, resource, or measure compared to all other countries in the given context.
- 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_69f01d8328c48190bc0e5f9b9b848582 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69fe78e545888190a239af1a84280fa0 |
completed | May 8, 2026, 11:59 p.m. |
| PD | Predicate disambiguation | batch_69fe7842742081908043eb950ed69f92 |
completed | May 8, 2026, 11:56 p.m. |
Created at: April 28, 2026, 4:37 a.m.