Triple
T8898092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quaker City |
E211854
|
entity |
| Predicate | hasPassengerDemographic |
P7875
|
FINISHED |
| Object | middle-class Americans |
—
|
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: middle-class Americans | Statement: [Quaker City, hasPassengerDemographic, middle-class Americans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerDemographic Context triple: [Quaker City, hasPassengerDemographic, middle-class Americans]
-
A.
hasDemographic
chosen
Indicates that an entity is associated with or characterized by a particular demographic group or attribute.
-
B.
hasPassengerUsageStatistics
Indicates the relationship by which an entity is associated with data describing how passengers use it, such as counts, frequencies, or patterns of passenger activity.
-
C.
hasDemographicPattern
Indicates that there is a characteristic distribution or trend of attributes (such as age, gender, income, or ethnicity) within a population or group.
-
D.
hasPassengerUsageCategory
Indicates the classification of how a passenger-related resource or service is used (e.g., its usage type or category for passengers).
-
E.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
- 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_69ca83918d3081909b326fa3750cb8c8 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc642618908190b3df50cbbabff93d |
completed | April 1, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69cc5c2bfb38819083d5eb1af8ccf4d6 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:54 p.m.