Triple
T6341944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berwyn Bungalow Historic District |
E142647
|
entity |
| Predicate | hasHousingDensity |
P70081
|
FINISHED |
| Object | high density of similar houses |
—
|
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 density of similar houses | Statement: [Berwyn Bungalow Historic District, hasHousingDensity, high density of similar houses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHousingDensity Context triple: [Berwyn Bungalow Historic District, hasHousingDensity, high density of similar houses]
-
A.
hasPopulationDensity
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
B.
hasPopulationDensityType
Indicates the classification of an area based on how densely populated it is (e.g., urban, suburban, rural).
-
C.
hasHousingUnits
Indicates that an entity possesses or contains a specified number or set of housing units.
-
D.
isDenselyPopulated
Indicates that a place has a high concentration of inhabitants relative to its area.
-
E.
hasOfficeDensity
Indicates the degree to which office spaces or workplaces are concentrated within a given area or entity.
- 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_69c008d5ab108190b346c465696824a9 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0674445748190bce2d638048be77c |
completed | March 22, 2026, 10:03 p.m. |
| PD | Predicate disambiguation | batch_69c060ea1a988190889e47b7e0c819b8 |
completed | March 22, 2026, 9:36 p.m. |
| PDg | Predicate description generation | batch_69c0623bb29081908bfdfb84a07ece90 |
completed | March 22, 2026, 9:42 p.m. |
Created at: March 22, 2026, 4:30 p.m.