Triple
T767121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Plaza Mayor de Lima |
E16199
|
entity |
| Predicate | hasFountain |
P19692
|
FINISHED |
| Object | Central bronze fountain |
—
|
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: Central bronze fountain | Statement: [Plaza Mayor de Lima, hasFountain, Central bronze fountain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFountain Context triple: [Plaza Mayor de Lima, hasFountain, Central bronze fountain]
-
A.
numberOfFountains
Indicates the quantitative relationship specifying how many fountains are associated with a given entity.
-
B.
hasWaterfall
Indicates that one entity possesses, contains, or features a waterfall associated with it.
-
C.
hasPier
Indicates that a location or structure possesses or includes a pier as part of its features.
-
D.
hasPavilion
Indicates that one entity possesses, includes, or is associated with a pavilion as part of its structure, property, or facilities.
-
E.
hasChampagneBar
Indicates that an entity includes, features, or is equipped with a champagne bar.
- 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_69a49369a0848190af883934cee3db4c |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a765ba688190ab328bb159583077 |
completed | March 1, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69a4a5074c788190a74fc20ad24e2d26 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a7648a6c8190a9051a3d177ff7e2 |
completed | March 1, 2026, 8:53 p.m. |
Created at: March 1, 2026, 7:37 p.m.