Triple
T32103291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chernyakhovsk |
E819911
|
entity |
| Predicate | hasHistoricalNameInLanguage |
P52208
|
FINISHED |
| Object | Insterburg (German) |
—
|
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: Insterburg (German) | Statement: [Chernyakhovsk, hasHistoricalNameInLanguage, Insterburg (German)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHistoricalNameInLanguage Context triple: [Chernyakhovsk, hasHistoricalNameInLanguage, Insterburg (German)]
-
A.
hasHistoricNameVariant
Indicates that an entity has an alternative name that was used in a historical period or past context.
-
B.
languageOfHistoricName
chosen
Indicates the language in which a historic or former name of an entity is expressed.
-
C.
isHistoricalName
Indicates that a given name is a former or past designation historically used for an entity.
-
D.
historicalNameInEnglish
Indicates that an entity is associated with a historical English-language name by which it was known in the past.
-
E.
historicalLanguageOfHierarchy
Indicates that a language served as the traditional or historically established language used by a particular hierarchy or hierarchical institution.
- 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_69f34901106881908ea893ad504a08be |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe6fea4a288190bf8615c5d6bf41b4 |
completed | May 8, 2026, 11:21 p.m. |
| PD | Predicate disambiguation | batch_69fe6f774de08190975a2393b9a1fd22 |
completed | May 8, 2026, 11:19 p.m. |
Created at: May 1, 2026, 12:26 a.m.