Triple
T7898052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NATS |
E183381
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | NATS Server (nats-server) |
E183381
|
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: NATS Server (nats-server) | Statement: [NATS, hasComponent, NATS Server (nats-server)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NATS Server (nats-server) Context triple: [NATS, hasComponent, NATS Server (nats-server)]
-
A.
NATS
chosen
NATS is a high-performance, cloud-native messaging system used for building scalable, distributed applications and microservices.
-
B.
Caddy
Caddy is a central character in William Faulkner’s novel "The Sound and the Fury," known for her complex role within the Compson family and her symbolic significance to the story’s themes of loss and decay.
-
C.
Jetty
Jetty is a lightweight, embeddable Java-based web server and servlet container widely used for hosting web applications and supporting modern web protocols.
-
D.
Jetty
Jetty is a celebrated painting by contemporary artist Peter Doig, known for its atmospheric, dreamlike depiction of a solitary figure on a lakeside structure.
-
E.
Ray Serve
Ray Serve is a scalable model serving library built on the Ray framework that enables deploying and managing machine learning models in production.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a296db8819084c620b12f77acb5 |
completed | March 31, 2026, 3:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5bb719a08190a0545a361f559bf7 |
completed | March 31, 2026, 5:29 a.m. |
Created at: March 30, 2026, 5:01 p.m.