Triple
T7041254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kovács |
E163516
|
entity |
| Predicate | rankAsSurnameInHungary |
P29278
|
FINISHED |
| Object | one of the most common surnames |
—
|
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: one of the most common surnames | Statement: [Kovács, rankAsSurnameInHungary, one of the most common surnames]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankAsSurnameInHungary Context triple: [Kovács, rankAsSurnameInHungary, one of the most common surnames]
-
A.
nameInHungarian
Indicates that one entity is the Hungarian-language name or designation of another entity.
-
B.
isAmongMostCommonSurnamesIn
chosen
Indicates that a surname ranks within the group of most frequently occurring surnames in a specified region or population.
-
C.
usedAsSurnameInCountry
Indicates that a particular name functions as a family surname within the specified country.
-
D.
rankByFrequencyInPoland
Indicates the relative ordering of entities based on how often they occur or appear in Poland.
-
E.
nameInSlovak
Indicates that an entity is known or referred to by a specific name in the Slovak 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_69c6885e7c1c8190be32a8f79ab4e0cf |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e4a3c36c819080942c59f1830ae8 |
completed | March 27, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bb602081908bfa6186a1f5a4b4 |
completed | March 27, 2026, 7:59 p.m. |
Created at: March 27, 2026, 2:36 p.m.