Triple
T11942766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parque Trianon |
E284216
|
entity |
| Predicate | hasNoiseContext |
P102388
|
FINISHED |
| Object | located in busy commercial district |
—
|
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: located in busy commercial district | Statement: [Parque Trianon, hasNoiseContext, located in busy commercial district]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoiseContext Context triple: [Parque Trianon, hasNoiseContext, located in busy commercial district]
-
A.
hasNoiseTerm
Indicates that a given expression, model, or equation includes an additional noise term representing random or unexplained variation.
-
B.
hasNoisePerformance
Indicates the degree to which one entity’s operation or behavior produces or is characterized by a certain level or quality of noise.
-
C.
hasNoiseModes
Indicates that an entity supports or is associated with one or more distinct noise-related operating modes or settings.
-
D.
targetsNoiseType
Indicates that an entity is directed at, designed for, or specifically affects a particular type or category of noise.
-
E.
noiseLevel
Indicates the intensity or amount of sound present in a given environment or from a specific source.
- F. None of above. chosen
Provenance (4 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90342bb908190a019ac91a2b82f3d |
completed | April 10, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3e48e08190b2fee43af4f57323 |
completed | April 10, 2026, 8:56 a.m. |
| PDg | Predicate description generation | batch_69d8dd0ba0f88190b7d5e358c27ca184 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 8, 2026, 9:45 p.m.