Triple
T3057368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Merrillville, Indiana |
E60512
|
entity |
| Predicate | hasRetailCorridor |
P30026
|
FINISHED |
| Object | U.S. 30 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: U.S. 30 retail corridor | Statement: [Merrillville, Indiana, hasRetailCorridor, U.S. 30 retail corridor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRetailCorridor Context triple: [Merrillville, Indiana, hasRetailCorridor, U.S. 30 retail corridor]
-
A.
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.
-
B.
hasRetailArea
Indicates that an entity possesses or includes a designated space used for retail or commercial sales activities.
-
C.
hasRetailPresenceIn
Indicates that an entity conducts retail operations or maintains a retail outlet, store, or sales presence within a specified location.
-
D.
hasRetailBoutiquesIn
Indicates that an entity operates or maintains retail boutiques located within a specified place or region.
-
E.
hasRetailCategory
Indicates that an entity is associated with a specific retail category or type of retail business.
- 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_69ad8578137c81908259dcb27c7d6d7c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ad9e162d148190969fb422a45d052c |
completed | March 8, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69ad962326e081909d5521c3d3ea3158 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:02 p.m.