Triple
T2851515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago Lawn |
E63100
|
entity |
| Predicate | hasCommercialStreet |
P30026
|
FINISHED |
| Object | 63rd Street commercial 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: 63rd Street commercial corridor | Statement: [Chicago Lawn, hasCommercialStreet, 63rd Street commercial corridor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommercialStreet Context triple: [Chicago Lawn, hasCommercialStreet, 63rd Street commercial corridor]
-
A.
hasMainStreet
Indicates that a place or locality possesses a primary street commonly recognized as its main thoroughfare.
-
B.
hasStreet
Indicates that an entity is located on, associated with, or identified by a particular street.
-
C.
hasMajorRetailCorridor
chosen
Indicates that a place contains or is associated with a primary commercial street or area characterized by a high concentration of retail businesses.
-
D.
hasShoppingDistrict
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
-
E.
hasNotableStreet
Indicates that an entity is associated with a particular street that is considered notable or significant.
- 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_69ab4c407c408190857d25e027155ce9 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf5ca2648190bd32c6ec4b0dd3b6 |
completed | March 7, 2026, 8:18 a.m. |
| PD | Predicate disambiguation | batch_69abdd0e86808190bcefffafbd3cd441 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.