Triple
T2599553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwynedd |
E58308
|
entity |
| Predicate | hasHighProportionOfSpeakersOf |
P39515
|
FINISHED |
| Object | Welsh |
—
|
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: Welsh | Statement: [Gwynedd, hasHighProportionOfSpeakersOf, Welsh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHighProportionOfSpeakersOf Context triple: [Gwynedd, hasHighProportionOfSpeakersOf, Welsh]
-
A.
hasApproximateNativeSpeakers
Indicates that an entity is associated with an estimated or approximate number of people who speak it as their native language.
-
B.
hasNativeSpeakers
Indicates that a language or dialect is spoken as a first language by one or more people or populations.
-
C.
haveSpeakerPopulation
Indicates that an entity has a specified number or population size of people who speak a particular language.
-
D.
includesLanguageWithLargeSpeakerPopulation
Indicates that the entity contains or is associated with at least one language that has a large number of speakers.
-
E.
secondLanguageSpeakers
Indicates that the referenced language is spoken as a second (non-native) language by the specified group or number of people.
- F. None of above. chosen
Provenance (4 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_69ab4ac14040819098b13f4a27d5c8ff |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd457564c819080d8c8818c02545a |
completed | March 7, 2026, 7:31 a.m. |
| PD | Predicate disambiguation | batch_69abd0d4e8648190b612eb09aa085451 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd1c7b6e48190be9a0c31069df797 |
completed | March 7, 2026, 7:20 a.m. |
Created at: March 6, 2026, 9:49 p.m.