Triple
T3930183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KYW (AM) |
E93374
|
entity |
| Predicate | programmingFeature |
P51750
|
FINISHED |
| Object | traffic reports |
—
|
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: traffic reports | Statement: [KYW (AM), programmingFeature, traffic reports]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: programmingFeature Context triple: [KYW (AM), programmingFeature, traffic reports]
-
A.
engineeringFeature
Indicates that one entity serves as an engineering-related feature, component, or characteristic of another entity within a technical or designed system.
-
B.
technologicalFeature
Indicates that one entity possesses, exhibits, or is characterized by a specific technological capability, component, or functionality in relation to another entity.
-
C.
languageFeature
Indicates that one entity is a characteristic, property, or capability of a language associated with the other entity.
-
D.
scriptFeature
Indicates that a script includes, supports, or is characterized by a particular feature or capability.
-
E.
programmingFocus
Indicates a relationship where an entity’s primary attention, effort, or specialization is directed toward a particular area or aspect of programming.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeeda7cf3c81909df30744bddbad7e |
completed | March 9, 2026, 3:56 p.m. |
| PD | Predicate disambiguation | batch_69aee7609c4081908000ce12ae827c3f |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aeeb5f859c819095b61f5ba8eace4f |
completed | March 9, 2026, 3:46 p.m. |
Created at: March 9, 2026, 3:23 p.m.