Triple
T23436821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pepco Benning Road power plant site |
E563484
|
entity |
| Predicate | hasTypeOfContamination |
P98276
|
FINISHED |
| Object | petroleum-related contaminants (likely) |
—
|
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: petroleum-related contaminants (likely) | Statement: [Pepco Benning Road power plant site, hasTypeOfContamination, petroleum-related contaminants (likely)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfContamination Context triple: [Pepco Benning Road power plant site, hasTypeOfContamination, petroleum-related contaminants (likely)]
-
A.
containsContaminant
Indicates that one entity includes, holds, or is tainted by an unwanted or harmful contaminating substance or element.
-
B.
hasCleanlinessLevel
Indicates the degree or state of cleanliness associated with an entity.
-
C.
hasTypeOfDamage
Indicates that an entity experiences or exhibits a specific kind or category of damage.
-
D.
containsWasteFrom
Indicates that one entity holds, includes, or is contaminated by waste originating from another entity.
-
E.
hasPollutionIssue
chosen
Indicates that an entity is affected by, associated with, or characterized by a pollution-related problem or concern.
- 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_69e24553980c8190bb66a2ae0bdab125 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a5dcd4608190a543cc747e0daab8 |
completed | April 29, 2026, 6:31 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:50 p.m.