Triple
T18734980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glenmont, New York |
E458136
|
entity |
| Predicate | hasRetailDevelopment |
P33790
|
FINISHED |
| Object | big-box stores |
—
|
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: big-box stores | Statement: [Glenmont, New York, hasRetailDevelopment, big-box stores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRetailDevelopment Context triple: [Glenmont, New York, hasRetailDevelopment, big-box stores]
-
A.
hasRetailStores
Indicates that an entity operates or possesses one or more physical retail store locations.
-
B.
hasRetailPresenceIn
Indicates that an entity conducts retail operations or maintains a retail outlet, store, or sales presence within a specified location.
-
C.
hasRetailNetwork
Indicates that an entity operates or is associated with a system of retail outlets or distribution channels through which products or services are sold.
-
D.
hasRetailUnits
chosen
Indicates that one entity possesses, operates, or is associated with one or more retail units (such as stores or outlets).
-
E.
hasRetailServices
Indicates that an entity provides or offers retail-related services to customers.
- 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_69d8d394dc308190b6725073f5db324c |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d7ae10081908bc6857d1d147eef |
completed | April 20, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69e48d03766c8190a43f7681842f4f8d |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:51 a.m.