Triple
T19603503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trnava |
E470542
|
entity |
| Predicate | universityFoundationYear |
P26573
|
FINISHED |
| Object | 1635 |
—
|
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: 1635 | Statement: [Trnava, universityFoundationYear, 1635]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: universityFoundationYear Context triple: [Trnava, universityFoundationYear, 1635]
-
A.
universityFoundedAs
Indicates the original form, name, or type under which a university was first established.
-
B.
campusOpeningYear
Indicates the calendar year in which a campus first officially opened or began operation.
-
C.
foundedAsUniversityIn
chosen
Indicates that an institution was originally established as a university in a specific year or time period.
-
D.
earliestCollegeFoundedInYear
Indicates that the referenced college is the first (earliest founded) college established in the specified year.
-
E.
schoolFounded
Indicates that an educational institution was established or created at a specific time or by a particular founding entity.
- 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64081af6c8190868b73b07c874cd5 |
completed | April 20, 2026, 3:04 p.m. |
| PD | Predicate disambiguation | batch_69e514e166dc8190a0f147e0b4c8bbe7 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:43 p.m.