Triple
T15169131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State Street (Chicago) |
E362438
|
entity |
| Predicate | hasRetailHistory |
P116986
|
FINISHED |
| Object | former flagship department 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: former flagship department stores | Statement: [State Street (Chicago), hasRetailHistory, former flagship department stores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRetailHistory Context triple: [State Street (Chicago), hasRetailHistory, former flagship department stores]
-
A.
hasRetailPresenceIn
Indicates that an entity conducts retail operations or maintains a retail outlet, store, or sales presence within a specified location.
-
B.
hasRetailStores
Indicates that an entity operates or possesses one or more physical retail store locations.
-
C.
hasRetailCharacteristic
Indicates that an entity possesses a specific attribute, feature, or quality relevant to retail contexts (such as pricing, packaging, or point-of-sale properties).
-
D.
hasRetailFormat
Indicates that one entity operates or is organized according to a particular retail format or store type.
-
E.
hasRetailUnits
Indicates that one entity possesses, operates, or is associated with one or more retail units (such as stores or outlets).
- F. None of above. chosen
Provenance (4 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0064dba588190a4341775b472a6d3 |
completed | April 15, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69deb9779acc81908ed2dad382c42dca |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec72059c08190a34f513a00185b08 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:08 a.m.