Triple
T1002546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Connecticut Avenue NW |
E21635
|
entity |
| Predicate | hasZoningAlongCorridor |
P727
|
FINISHED |
| Object | mixed-use |
—
|
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: mixed-use | Statement: [Connecticut Avenue NW, hasZoningAlongCorridor, mixed-use]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasZoningAlongCorridor Context triple: [Connecticut Avenue NW, hasZoningAlongCorridor, mixed-use]
-
A.
hasCorridor
Indicates that one entity includes, is connected by, or provides access through a corridor to another entity.
-
B.
hasServiceOnCorridor
Indicates that a service operates along, or is provided on, a specific corridor or route.
-
C.
zoningCharacter
chosen
Indicates how the regulatory or functional nature of a geographic area is defined or classified in terms of land-use zoning.
-
D.
hasNeighborhoodAlong
Indicates that one entity has a neighboring area or region that extends along the boundary or length of another entity.
-
E.
hasZone
Indicates that one entity possesses, contains, or is associated with a specific zone or designated area.
- 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_69a493c53e648190ae8cb76c433fd9a7 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4fe0a548190aee8abf1890e141e |
completed | March 1, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69a4b2b1f4f88190822598cfd2a0fd2b |
completed | March 1, 2026, 9:42 p.m. |
Created at: March 1, 2026, 7:41 p.m.