Triple
T9899056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azure Cosmos DB |
E182241
|
entity |
| Predicate | supportsAPI |
P203
|
FINISHED |
| Object |
Table API
Table API is a schema-less, key-value data access interface in Azure Cosmos DB designed for scalable, low-latency storage and retrieval of tabular data.
|
E828308
|
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: Table API | Statement: [Azure Cosmos DB, supportsAPI, Table API]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Table API Context triple: [Azure Cosmos DB, supportsAPI, Table API]
-
A.
Table API
Table API is Apache Flink’s high-level, declarative interface for expressing data processing and analytics using relational-style operations on streaming and batch data.
-
B.
Tabularium
The Tabularium was the official records office of ancient Rome, a monumental state archive building overlooking the Roman Forum.
-
C.
SQL API
SQL API is a query interface in Apache Flink that lets users define streaming and batch data processing logic using standard SQL syntax.
-
D.
Tabular Editor
Tabular Editor is a specialized development tool for creating, managing, and optimizing tabular models used in platforms like Azure Analysis Services and Power BI.
-
E.
DataSet API
DataSet API is Apache Flink’s now-legacy batch processing API for defining and executing scalable, distributed data transformations.
- 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: Table API Triple: [Azure Cosmos DB, supportsAPI, Table API]
Generated description
Table API is a schema-less, key-value data access interface in Azure Cosmos DB designed for scalable, low-latency storage and retrieval of tabular data.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Table API Target entity description: Table API is a schema-less, key-value data access interface in Azure Cosmos DB designed for scalable, low-latency storage and retrieval of tabular data.
-
A.
Table API
Table API is Apache Flink’s high-level, declarative interface for expressing data processing and analytics using relational-style operations on streaming and batch data.
-
B.
Tabularium
The Tabularium was the official records office of ancient Rome, a monumental state archive building overlooking the Roman Forum.
-
C.
SQL API
SQL API is a query interface in Apache Flink that lets users define streaming and batch data processing logic using standard SQL syntax.
-
D.
Tabular Editor
Tabular Editor is a specialized development tool for creating, managing, and optimizing tabular models used in platforms like Azure Analysis Services and Power BI.
-
E.
DataSet API
DataSet API is Apache Flink’s now-legacy batch processing API for defining and executing scalable, distributed data transformations.
- 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_69ca82876f8081909cf75df0f99bb13f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb4adc03481909e0f657db01e5bab |
completed | April 2, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eb1b9534819093c5150f1ed8f685 |
completed | April 5, 2026, 4:54 a.m. |
| NEDg | Description generation | batch_69d1ecb3e4a08190add9b971d96f331d |
completed | April 5, 2026, 5:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1ed413edc81908177be16d5127a58 |
completed | April 5, 2026, 5:04 a.m. |
Created at: March 30, 2026, 8:40 p.m.