Triple
T13537350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whitman neighborhood |
E323294
|
entity |
| Predicate | hasBuildingDensity |
P81830
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Whitman neighborhood, hasBuildingDensity, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBuildingDensity Context triple: [Whitman neighborhood, hasBuildingDensity, high]
-
A.
hasHousingDensity
Indicates the relationship between an area and the concentration of housing units within that area, typically measured as units per unit of land.
-
B.
hasHighDensityOf
chosen
Indicates that one entity contains or exhibits a large concentration or amount of another entity within a given area, volume, or context.
-
C.
hasOfficeDensity
Indicates the degree to which office spaces or workplaces are concentrated within a given area or entity.
-
D.
containsBuilding
Indicates that one location or area includes a building within its boundaries.
-
E.
numberOfBuildings
Indicates the total count of buildings associated with a given entity or within a specified context.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafbe39948190808062d4eff91841 |
completed | April 12, 2026, 2:44 p.m. |
| PD | Predicate disambiguation | batch_69dbae1046c48190b4ee98c6c9cb9d85 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:45 p.m.