Triple
T17500198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache ORC |
E426165
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Apache ORC |
—
|
NE NERFINISHED |
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: Apache ORC | Statement: [Apache ORC, name, Apache ORC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apache ORC Context triple: [Apache ORC, name, Apache ORC]
-
A.
Apache ORC project
chosen
The Apache ORC project is an open-source initiative that develops the Optimized Row Columnar (ORC) file format for efficient, high-performance storage and processing of large-scale data in big data ecosystems.
-
B.
Apache Parquet
Apache Parquet is a columnar storage file format optimized for efficient data compression and query performance in big data processing frameworks such as Apache Hadoop and Apache Spark.
-
C.
Apache Iceberg
Apache Iceberg is an open table format for huge analytic datasets that enables reliable, high-performance querying and data management in data lake environments.
-
D.
Apache Impala
Apache Impala is a massively parallel, SQL-on-Hadoop query engine designed for low-latency, interactive analysis of large-scale data stored in distributed systems.
-
E.
Apache Avro
Apache Avro is a data serialization system and file format in the Apache Hadoop ecosystem that provides compact, fast, binary data encoding with rich schema support and dynamic typing.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452112ff0819089c2951baba90102 |
completed | April 19, 2026, 3:54 a.m. |
Created at: April 10, 2026, 5:48 a.m.