Triple
T23788576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wendy Scott-Carr |
E588025
|
entity |
| Predicate | primarySettingCity |
P136244
|
FINISHED |
| Object | Chicago |
—
|
NE NERFINISHED |
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: Chicago | Statement: [Wendy Scott-Carr, primarySettingCity, Chicago]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primarySettingCity Context triple: [Wendy Scott-Carr, primarySettingCity, Chicago]
-
A.
primaryLocationCity
chosen
Indicates the city that serves as the main or primary location associated with the subject.
-
B.
primaryCity
Indicates that one city serves as the main or most important city associated with a given region, entity, or context.
-
C.
primaryUseCity
Indicates that a city is the main or principal location where something (such as a service, facility, or entity) is primarily used or operates.
-
D.
primaryTown
Indicates that a given town is the main or most important town associated with an entity (such as a person, organization, or region).
-
E.
nativeCity
Indicates that a city is the place where a person was born or is originally from.
- 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_69e2490f4ad48190b690878eec3596c6 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1c633f7908190bac7fdac5990920a |
completed | April 29, 2026, 8:49 a.m. |
| PD | Predicate disambiguation | batch_69f155fe300481909bd617443228df65 |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:17 p.m.