Triple
T29907529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramos neighborhood |
E759579
|
entity |
| Predicate | hasUrbanProfile |
P154385
|
FINISHED |
| Object | densely populated |
—
|
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: densely populated | Statement: [Ramos neighborhood, hasUrbanProfile, densely populated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanProfile Context triple: [Ramos neighborhood, hasUrbanProfile, densely populated]
-
A.
hasUrbanRole
Indicates that an entity plays a specific functional or social role within an urban or city context.
-
B.
hasUrbanFunction
Indicates that an entity serves a specific role or purpose within an urban context, such as providing services, infrastructure, or activities typical of a city environment.
-
C.
hasUrbanAreaCharacter
chosen
Indicates that something possesses qualities, features, or conditions typical of an urban area.
-
D.
hasUrbanPopulationIn
Indicates that an entity has a specified urban population within a particular geographic area or administrative unit.
-
E.
hasUrbanClassification
Indicates that an entity is assigned a specific urban status or category within a defined classification system.
- 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_69f224600590819085e148a01c056ef6 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f67757ec208190986cdbd06d9717ab |
completed | May 2, 2026, 10:14 p.m. |
| PD | Predicate disambiguation | batch_69f66ec8298c8190b41fe9d182c05676 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:09 p.m.