Triple
T14342899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | java.nio |
E355645
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | java.nio.FloatBuffer |
E355645
|
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: java.nio.FloatBuffer | Statement: [java.nio, contains, java.nio.FloatBuffer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: java.nio.FloatBuffer Context triple: [java.nio, contains, java.nio.FloatBuffer]
-
A.
java.nio
chosen
java.nio is a Java API package that provides non-blocking I/O, buffer management, and scalable channel-based input/output operations.
-
B.
NIO
NIO is a Chinese electric vehicle manufacturer known for its premium smart EVs and innovative battery-swapping technology.
-
C.
FlatBuffers
FlatBuffers is an efficient cross-platform serialization library from Google designed for fast, memory-efficient data access without an unpacking step, commonly used in games, mobile, and high-performance services.
-
D.
FBO
FBO, short for Film Booking Offices of America, was a 1920s American film distribution and production company that later became part of RKO Pictures.
-
E.
FlumeJava
FlumeJava is a Java-based library from Google for building, optimizing, and running large-scale data-parallel pipelines, later inspiring systems like Apache Beam.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8e87febc8190a63c668cbd0fd713 |
completed | April 14, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd469d899081909103563f209dd944 |
completed | May 8, 2026, 2:12 a.m. |
Created at: April 10, 2026, 1:14 a.m.