Triple
T623748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chemnitz River |
E14568
|
entity |
| Predicate | hasEconomicUse |
P3592
|
FINISHED |
| Object | urban water management in Chemnitz |
—
|
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: urban water management in Chemnitz | Statement: [Chemnitz River, hasEconomicUse, urban water management in Chemnitz]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEconomicUse Context triple: [Chemnitz River, hasEconomicUse, urban water management in Chemnitz]
-
A.
economicUse
chosen
Indicates that one entity utilizes another for economic purposes, such as production, trade, revenue generation, or cost-saving activities.
-
B.
hasEconomicImportanceFor
Indicates that one entity holds economic value, benefit, or significance for another entity.
-
C.
hasEconomicRole
Indicates that an entity participates in or fulfills a specific function, position, or responsibility within an economic system or activity.
-
D.
hasEconomicActivity
Indicates that an entity engages in, supports, or is associated with a specific type of economic activity or business operation.
-
E.
hasHumanUse
Indicates that something is used, employed, or utilized by humans for a particular purpose or benefit.
- 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_69a4934b17c881909ace8270e8ddd202 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e41753881909f0faed720cc31bc |
completed | March 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69a49d0069d0819087c83b608f6fc053 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.