Triple
T6350062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Plaza Mayor de Tordesillas |
E142845
|
entity |
| Predicate | hasTypicalUseTime |
P70126
|
FINISHED |
| Object | daytime social activity |
—
|
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: daytime social activity | Statement: [Plaza Mayor de Tordesillas, hasTypicalUseTime, daytime social activity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalUseTime Context triple: [Plaza Mayor de Tordesillas, hasTypicalUseTime, daytime social activity]
-
A.
durationOfUse
Indicates the length of time for which something is used or remains in use.
-
B.
typicalUseDays
Indicates the usual or expected number of days over which something is used or intended to be used.
-
C.
periodOfMajorUse
Indicates the time span during which something was primarily or most intensively used.
-
D.
usedUntil
Indicates that something remained in use or operation up to a specified time or event, after which it was no longer used.
-
E.
yearOfUse
Indicates the specific year during which something was in use or actively utilized.
- F. None of above. chosen
Provenance (4 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_69c008d6dcbc8190aa1c2f1fd8916b42 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067bcec2c8190bb383605847b0f0b |
completed | March 22, 2026, 10:05 p.m. |
| PD | Predicate disambiguation | batch_69c060ea1a988190889e47b7e0c819b8 |
completed | March 22, 2026, 9:36 p.m. |
| PDg | Predicate description generation | batch_69c0623bb29081908bfdfb84a07ece90 |
completed | March 22, 2026, 9:42 p.m. |
Created at: March 22, 2026, 4:31 p.m.