Triple
T11581290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DWLS 97.1 |
E274630
|
entity |
| Predicate | effectiveRadiatedPower |
P100428
|
FINISHED |
| Object | 100 kW |
—
|
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: 100 kW | Statement: [DWLS 97.1, effectiveRadiatedPower, 100 kW]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectiveRadiatedPower Context triple: [DWLS 97.1, effectiveRadiatedPower, 100 kW]
-
A.
hasRadioOutputPowerRange
Indicates the range of possible radio output power levels that an entity can transmit.
-
B.
transmissionPowerType
Indicates the type or category of power used to transmit a signal or data between entities.
-
C.
powerDensity
Indicates the amount of power distributed per unit area or volume in a given context.
-
D.
intendedPower
Indicates the level or amount of power that an entity is designed, planned, or expected to produce, use, or deliver under intended operating conditions.
-
E.
effectiveArea
Indicates the portion of a surface or region that actually contributes to a specified effect, such as performance, interaction, or impact, within a given context.
- 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_69d6aae5ac3c81908d2b0a3a665665b2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8904c51b881909e7be84c6f3de79f |
completed | April 10, 2026, 5:53 a.m. |
| PD | Predicate disambiguation | batch_69d85dcbacd0819094d4a1237055affa |
completed | April 10, 2026, 2:17 a.m. |
| PDg | Predicate description generation | batch_69d87f2e67108190ac36bf47aac12fa8 |
completed | April 10, 2026, 4:40 a.m. |
Created at: April 8, 2026, 9:38 p.m.