Triple
T33790787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | camphechlor |
E865924
|
entity |
| Predicate | hasExposureRoute |
P52267
|
FINISHED |
| Object | inhalation of contaminated air |
—
|
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: inhalation of contaminated air | Statement: [camphechlor, hasExposureRoute, inhalation of contaminated air]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExposureRoute Context triple: [camphechlor, hasExposureRoute, inhalation of contaminated air]
-
A.
routeOfExposure
chosen
Indicates the pathway or method by which an agent, substance, or factor comes into contact with or enters an organism or system.
-
B.
hasExposuresIn
Indicates that an entity is subject to or involved in certain risks, conditions, or influencing factors within a specified context, environment, or domain.
-
C.
hasWeatherExposure
Indicates that something is subject to or affected by outdoor weather conditions or elements.
-
D.
hasExposuresNear
Indicates that one entity has exposures occurring in close spatial or temporal proximity to another entity or reference point.
-
E.
hasRiskFrom
Indicates that one entity is exposed to or may suffer potential harm, loss, or adverse effects as a result of another entity.
- 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_69f3498f99f481909cb271f4965a7594 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fcf1b3d9a08190850b388308656266 |
completed | May 7, 2026, 8:10 p.m. |
| PD | Predicate disambiguation | batch_69fcf0226d8c8190b23dceafb1794995 |
completed | May 7, 2026, 8:03 p.m. |
Created at: May 1, 2026, 1:45 a.m.