Triple
T27428591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Río Apatlaco |
E690559
|
entity |
| Predicate | hasTypeOfPollution |
P98276
|
FINISHED |
| Object | organic pollution |
—
|
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: organic pollution | Statement: [Río Apatlaco, hasTypeOfPollution, organic pollution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfPollution Context triple: [Río Apatlaco, hasTypeOfPollution, organic pollution]
-
A.
hasPollutionIssue
chosen
Indicates that an entity is affected by, associated with, or characterized by a pollution-related problem or concern.
-
B.
targetPollutant
Indicates that something is the specific pollutant that is being aimed at, affected, or addressed by an action, process, or regulation.
-
C.
pollutionLevel
Indicates the degree or intensity of environmental contamination present in a given context or location.
-
D.
wasHeavilyPollutedDuring
Indicates that a place or environment experienced a high level of pollution during a specified time period.
-
E.
pollutionSource
Indicates that one entity is the origin or cause of pollution affecting another entity or environment.
- 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_69ef52003fb48190b0f1295246182a86 |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
Created at: April 27, 2026, 12:41 p.m.