Triple
T10931089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meycauayan River |
E258206
|
entity |
| Predicate | hasPollutionSource |
P48801
|
FINISHED |
| Object | tanneries |
—
|
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: tanneries | Statement: [Meycauayan River, hasPollutionSource, tanneries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPollutionSource Context triple: [Meycauayan River, hasPollutionSource, tanneries]
-
A.
pollutionSource
chosen
Indicates that one entity is the origin or cause of pollution affecting another entity or environment.
-
B.
targetPollutant
Indicates that something is the specific pollutant that is being aimed at, affected, or addressed by an action, process, or regulation.
-
C.
wasHeavilyPollutedDuring
Indicates that a place or environment experienced a high level of pollution during a specified time period.
-
D.
hasEnvironmentalImpactOn
Indicates that one entity affects or alters the environmental conditions, quality, or ecological state of another entity.
-
E.
hasEnvironmentalImpactType
Indicates that something affects the environment in a specific way categorized by a particular type of impact.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770a062f481908beb76c6dbaeb6a6 |
completed | April 9, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69d72e799f808190b6ab64fc7586a303 |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:23 p.m.