Triple
T10371785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WritableStreamDefaultWriter |
E244400
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Streams API |
E856192
|
NE 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: Streams API | Statement: [WritableStreamDefaultWriter, partOf, Streams API]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Streams API Context triple: [WritableStreamDefaultWriter, partOf, Streams API]
-
A.
Streams API
chosen
The Streams API is a web platform feature that enables efficient, programmable handling of streaming data in JavaScript, allowing developers to read, write, and transform data chunks incrementally.
-
B.
DataStream API
DataStream API is Apache Flink’s core streaming abstraction for building stateful, event-driven data processing applications over unbounded and bounded data streams.
-
C.
Kafka Streams
Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
-
D.
Back Stream
Back Stream is a minor watercourse in Somerset, England, that serves as one of the tributaries feeding into the River Tone.
-
E.
Pipeline
Pipeline is a scikit-learn utility that chains multiple data processing and modeling steps into a single composite estimator for streamlined machine learning workflows.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e97ed09c8190a3627aa7b5eea62f |
completed | April 7, 2026, 11:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90d76fd88819086c61c40216a8932 |
completed | April 10, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:01 p.m.