Triple
T11266422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Iskra |
E266697
|
entity |
| Predicate | codenameMeaning |
P28204
|
FINISHED |
| Object |
Spark
Spark is the codename used for Operation Iskra, the World War II Soviet military offensive that aimed to break the German siege of Leningrad.
|
E915038
|
NE FINISHED |
How this triple was built (4 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: Spark | Statement: [Operation Iskra, codenameMeaning, Spark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spark Context triple: [Operation Iskra, codenameMeaning, Spark]
-
A.
Spark
"Spark" is a virtuosic jazz fusion composition by Japanese pianist Hiromi Uehara, showcasing her signature blend of technical brilliance and energetic, genre-blurring style.
-
B.
Spark
"Spark" is a 1998 piano-driven alternative rock song by Tori Amos, known for its haunting lyrics and emotional intensity.
-
C.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
D.
PySpark
PySpark is the Python API for Apache Spark, enabling large-scale data processing, analysis, and machine learning using Python.
-
E.
RDD
RDD is the three-letter IATA airport code for Redding Municipal Airport in Redding, California.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Spark Triple: [Operation Iskra, codenameMeaning, Spark]
Generated description
Spark is the codename used for Operation Iskra, the World War II Soviet military offensive that aimed to break the German siege of Leningrad.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Spark Target entity description: Spark is the codename used for Operation Iskra, the World War II Soviet military offensive that aimed to break the German siege of Leningrad.
-
A.
Spark
"Spark" is a virtuosic jazz fusion composition by Japanese pianist Hiromi Uehara, showcasing her signature blend of technical brilliance and energetic, genre-blurring style.
-
B.
Spark
"Spark" is a 1998 piano-driven alternative rock song by Tori Amos, known for its haunting lyrics and emotional intensity.
-
C.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
D.
PySpark
PySpark is the Python API for Apache Spark, enabling large-scale data processing, analysis, and machine learning using Python.
-
E.
RDD
RDD is the three-letter IATA airport code for Redding Municipal Airport in Redding, California.
- F. None of above. chosen
Provenance (5 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_69d6aac8c2f48190ad0596f1f89f0470 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e94e5e3c8190a31995d55d20d7ed |
completed | April 9, 2026, 6 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ccc7fdc48190a84b8b584f67b464 |
completed | April 19, 2026, 12:38 p.m. |
| NEDg | Description generation | batch_69e4d9ed6a048190ae7476d44cee6a6e |
completed | April 19, 2026, 1:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e4ddb1b4c8819087699bc73610c7f8 |
completed | April 19, 2026, 1:50 p.m. |
Created at: April 8, 2026, 9:31 p.m.