Triple
T10095124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1977 New York City blackout-related unrest |
E215845
|
entity |
| Predicate | affectedAreaType |
P6822
|
FINISHED |
| Object | economically distressed neighborhoods |
—
|
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: economically distressed neighborhoods | Statement: [1977 New York City blackout-related unrest, affectedAreaType, economically distressed neighborhoods]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedAreaType Context triple: [1977 New York City blackout-related unrest, affectedAreaType, economically distressed neighborhoods]
-
A.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
B.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
C.
coveredArea
Indicates that one entity occupies or extends over a specific spatial region or surface area associated with another entity.
-
D.
representedArea
Indicates that one entity serves as a representation or depiction of a particular area or region.
-
E.
typeOfAreaRepresented
Indicates that one entity specifies the kind or category of area that another entity represents.
- 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_69ca83a4947c8190823a7495dc5d96ed |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd0784c288190967d143beca32c4b |
completed | April 2, 2026, 2:12 a.m. |
| PD | Predicate disambiguation | batch_69cd4b9b853c8190a2af993ce9b21309 |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:02 p.m.