Triple
T14020860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leghorn |
E337325
|
entity |
| Predicate | hasTraditionalNameInLanguage |
P21030
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Leghorn, hasTraditionalNameInLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalNameInLanguage Context triple: [Leghorn, hasTraditionalNameInLanguage, English]
-
A.
hasTraditionalName
Indicates that an entity is associated with a name traditionally used or recognized for it, often rooted in long-standing cultural or historical practice.
-
B.
traditionalLanguageName
chosen
Indicates the name traditionally used in a particular language to refer to the subject entity.
-
C.
hasTraditionalTranslation
Indicates that one entity serves as the established or customary translation of another entity in a traditional or historically accepted sense.
-
D.
hasTraditionalLanguageRegion
Indicates the geographic region traditionally associated with the use or origin of a particular language.
-
E.
languageOfHistoricName
Indicates the language in which a historic or former name of an entity is expressed.
- 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2f3c7cd88190b236382058581740 |
completed | April 14, 2026, 12:12 p.m. |
| PD | Predicate disambiguation | batch_69de05a802ac819090604025aae6a4d5 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:19 p.m.