Triple
T18215594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madison/Wells |
E436145
|
entity |
| Predicate | servedCommuters |
P60625
|
FINISHED |
| Object | office workers in Chicago Loop |
—
|
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: office workers in Chicago Loop | Statement: [Madison/Wells, servedCommuters, office workers in Chicago Loop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servedCommuters Context triple: [Madison/Wells, servedCommuters, office workers in Chicago Loop]
-
A.
usedByCommuters
Indicates that something is regularly utilized by people traveling between home and work or school.
-
B.
commuterServiceTo
Indicates a transportation service that regularly carries commuters to a specified destination.
-
C.
commuterHubFor
chosen
Indicates a location that serves as a primary transit or gathering point for commuters traveling to or from another place.
-
D.
transportationServedBy
Indicates that a transportation facility, route, or area is provided service or coverage by a specific transportation provider or mode.
-
E.
hasCommuterTraffic
Indicates that there is regular, recurring traffic flow associated with people traveling between their homes and places of work or study.
- 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_69d8b9103a8081908bbb0836fef10efd |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e476a6548190bda03190c5f531ad |
completed | April 19, 2026, 2:19 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:32 a.m.