Triple
T33439678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Our Lady of Gietrzwałd |
E856325
|
entity |
| Predicate | apparitionYearStart |
P121574
|
FINISHED |
| Object | 1877 |
—
|
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: 1877 | Statement: [Our Lady of Gietrzwałd, apparitionYearStart, 1877]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: apparitionYearStart Context triple: [Our Lady of Gietrzwałd, apparitionYearStart, 1877]
-
A.
apparitionsYear
Indicates the year in which the apparitions or appearances associated with an entity took place.
-
B.
yearOfFirstApparition
chosen
Indicates the calendar year in which the entity first appeared or was introduced.
-
C.
approximateStartYear
Indicates that the associated year value represents an estimated or imprecise starting year for an event, state, or relationship rather than an exact one.
-
D.
firstPublicationYearOfAppearance
Indicates the year in which an entity (such as a work or character) first appeared in a published form.
-
E.
beganInYear
Indicates that an event, process, or state started in a specific calendar year.
- 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_69f34971b75881908be360bb041f003c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f70b966860819089cf92927f47c5f1 |
completed | May 3, 2026, 8:47 a.m. |
| PD | Predicate disambiguation | batch_69f70abe43e08190b2a30930d96247c1 |
completed | May 3, 2026, 8:43 a.m. |
Created at: May 1, 2026, 1:37 a.m.