Triple
T34759136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zara |
E1002015
|
entity |
| Predicate | isHistoricNameIn |
P66486
|
FINISHED |
| Object | Italian |
—
|
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: Italian | Statement: [Zara, isHistoricNameIn, Italian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isHistoricNameIn Context triple: [Zara, isHistoricNameIn, Italian]
-
A.
isHistoricalName
Indicates that a given name is a former or past designation historically used for an entity.
-
B.
isHistoricNameOfEventAt
Indicates that a given historic name refers to or was used for a specific event that occurred at a particular place or time.
-
C.
isHistoric
Indicates that something has significant importance or relevance in history, often due to its age, impact, or role in past events.
-
D.
hasHistoricNameVariant
chosen
Indicates that an entity has an alternative name that was used in a historical period or past context.
-
E.
isHistoricFor
Indicates that something has historical significance, relevance, or impact specifically in relation to a given entity or context.
- 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_69f76db0fb30819096709d43f9a1f45f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77ffa6b68819090257fed3802c239 |
completed | May 3, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69f7795978c481909e152cd1bd02dd07 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.