Triple
T14974386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Plaza de San Pablo |
E373405
|
entity |
| Predicate | hasUsagePattern |
P12995
|
FINISHED |
| Object | daily use by residents |
—
|
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: daily use by residents | Statement: [Plaza de San Pablo, hasUsagePattern, daily use by residents]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUsagePattern Context triple: [Plaza de San Pablo, hasUsagePattern, daily use by residents]
-
A.
hasUsePattern
Indicates a characteristic or recurring way in which something is typically used or applied.
-
B.
hasPattern
Indicates that one entity exhibits, follows, or is characterized by a specific recurring form, structure, or design defined by another entity.
-
C.
hasUsageLevel
Indicates the degree or intensity with which something is used or utilized.
-
D.
hasUsageNote
Indicates that there is an associated explanatory note describing how or when something should be used.
-
E.
usagePattern
chosen
Indicates how something is typically used or the recurring manner in which it is employed or consumed.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e8733081908e06b53746eb6eb6 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a5d995881909e33658f5aea5582 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:51 a.m.