Triple
T33173388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friendship Heights, Maryland |
E849095
|
entity |
| Predicate | shoppingDistrictType |
P35906
|
FINISHED |
| Object | upscale retail corridor |
—
|
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: upscale retail corridor | Statement: [Friendship Heights, Maryland, shoppingDistrictType, upscale retail corridor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shoppingDistrictType Context triple: [Friendship Heights, Maryland, shoppingDistrictType, upscale retail corridor]
-
A.
hasShoppingDistrictType
chosen
Indicates that an entity is associated with a particular type or category of shopping district.
-
B.
hasShoppingDistrictName
Indicates that an entity’s shopping district is identified by a specific name.
-
C.
hasShoppingDistrict
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
-
D.
isShoppingDistrict
Indicates that a location functions primarily as a shopping district, characterized by a concentration of retail stores and commercial shopping activity.
-
E.
shoppingCenterInstanceOf
Indicates that a specific shopping center is an instance of a particular type or class of shopping centers.
- 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_69f3495be8808190bbf427733df08aad |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6dd3cc0648190a275812d6711275a |
completed | May 3, 2026, 5:29 a.m. |
| PD | Predicate disambiguation | batch_69f6d82eaee081908f06a71546315aea |
completed | May 3, 2026, 5:07 a.m. |
Created at: May 1, 2026, 1:29 a.m.