Triple
T25557357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Italian maiolica |
E640608
|
entity |
| Predicate | timeOfPeakProduction |
P23186
|
FINISHED |
| Object | 16th century Italy |
—
|
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: 16th century Italy | Statement: [Italian maiolica, timeOfPeakProduction, 16th century Italy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeOfPeakProduction Context triple: [Italian maiolica, timeOfPeakProduction, 16th century Italy]
-
A.
historicalPeakIndustrialPeriod
Indicates the time period during which an entity reached its highest level of industrial activity or development.
-
B.
peakProductionRate
Indicates the maximum rate at which something can be produced over a given period under specified conditions.
-
C.
timePeriodOfProduction
chosen
Indicates the span of time during which something was produced or created.
-
D.
timeOfPeakSize
Indicates the specific time at which something reaches its maximum size or extent.
-
E.
numberOfEmployeesAtPeak
Indicates the highest recorded count of employees that an entity had at any point in time.
- 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_69e75dc101a881909fd33b02174e9768 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f6352fdb788190b9bad30243690743 |
completed | May 2, 2026, 5:32 p.m. |
| PD | Predicate disambiguation | batch_69f63182f1408190bddc1214fcbd6145 |
completed | May 2, 2026, 5:16 p.m. |
Created at: April 21, 2026, 3:40 p.m.