Triple
T2866954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old San Juan |
E63462
|
entity |
| Predicate | hasBuildingHeightCharacteristic |
P3373
|
FINISHED |
| Object | low-rise buildings |
—
|
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: low-rise buildings | Statement: [Old San Juan, hasBuildingHeightCharacteristic, low-rise buildings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBuildingHeightCharacteristic Context triple: [Old San Juan, hasBuildingHeightCharacteristic, low-rise buildings]
-
A.
hasBuildingHeightType
chosen
Indicates the classification or type used to characterize the height of a building in the relationship.
-
B.
buildingHeightContext
Indicates the contextual or situational factors under which a building’s height is defined, measured, or interpreted.
-
C.
buildingHeight
Indicates the vertical extent or height measurement of a building.
-
D.
hasTowerHeight
Indicates that an entity (such as a tower or structure) has a specific height value associated with it.
-
E.
architecturalHeight
Indicates the measured vertical extent of a structure based on its architectural design, typically from the lowest significant level to the highest architecturally integral point, excluding non-architectural elements like antennas or masts.
- 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_69ab4c42fb8c8190b36e161d47c03b81 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdfbcebcc81909a78a1787d823e3e |
completed | March 7, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69abdd123ec48190af50a1859aea50b7 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.