Triple
T170637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Székesfehérvár |
E3113
|
entity |
| Predicate | hasHistoricalNameLanguage |
P2834
|
FINISHED |
| Object | Latin |
—
|
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: Latin | Statement: [Székesfehérvár, hasHistoricalNameLanguage, Latin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHistoricalNameLanguage Context triple: [Székesfehérvár, hasHistoricalNameLanguage, Latin]
-
A.
hasHistoricalOrigin
Indicates that something originated, was first established, or came into existence during a specific historical period or context.
-
B.
historicallySpokenIn
Indicates that a language was used for spoken communication in a particular place or region during a past historical period.
-
C.
historicallyRecognizedAs
Indicates that an entity has been acknowledged or designated under a particular name, status, or role during a past historical period.
-
D.
hasEnglishName
Indicates that an entity is associated with a name expressed in the English language.
-
E.
historicallyBorneBy
chosen
Indicates that an entity has carried, possessed, or used another entity (such as a name, title, or symbol) at some point in the past.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a25a3f9d188190aacf5791273e2c65 |
completed | Feb. 28, 2026, 3 a.m. |
| PD | Predicate disambiguation | batch_69a25667717c8190bac5108366e2178d |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.