Triple
T4278195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JSON API |
E97089
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | GraphQL |
E208081
|
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: GraphQL | Statement: [JSON API, relatedTo, GraphQL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GraphQL Context triple: [JSON API, relatedTo, GraphQL]
-
A.
GraphQL
chosen
GraphQL is a query language and runtime for APIs that enables clients to request exactly the data they need from servers in a flexible, efficient way.
-
B.
SPARQL
SPARQL is a semantic query language and protocol used to retrieve and manipulate data stored in Resource Description Framework (RDF) format on the Semantic Web.
-
C.
AWS AppSync
AWS AppSync is a managed GraphQL service from Amazon Web Services that simplifies building real-time, data-driven applications by securely connecting to and orchestrating multiple data sources.
-
D.
Grok
Grok is an AI chatbot developed by xAI, designed to provide conversational access to real-time information and reasoning capabilities.
-
E.
Next.js
Next.js is a popular React-based framework for building server-rendered and statically generated web applications with features like routing, API routes, and performance optimizations built in.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350201ac88190b9d8980da5f0d03d |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7b708b481908c1683741f84ee55 |
completed | March 14, 2026, 7:32 p.m. |
Created at: March 12, 2026, 11:07 p.m.