Triple
T2114427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NNTP |
E42573
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object |
Usenet
Usenet is a worldwide distributed discussion system that predates the modern web, where users post and read messages in topic-based newsgroups.
|
E233830
|
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: Usenet | Statement: [NNTP, relatedTo, Usenet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Usenet Context triple: [NNTP, relatedTo, Usenet]
-
A.
NNTP
NNTP (Network News Transfer Protocol) is an application-layer protocol used for reading and posting articles on Usenet newsgroups over TCP/IP networks.
-
B.
Gnus
Gnus is a flexible and extensible message reader for news and email, tightly integrated with the Emacs text editor.
-
C.
Forum
Forum is an independent, avant-garde section of the Berlin International Film Festival that showcases experimental, innovative, and often politically engaged cinema from around the world.
-
D.
sns
sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
-
E.
Foros
Foros is a coastal resort settlement on the southern shore of Crimea, known for its scenic cliffs, beaches, and the landmark Church of the Resurrection overlooking the Black Sea.
- 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: Usenet Triple: [NNTP, relatedTo, Usenet]
Generated description
Usenet is a worldwide distributed discussion system that predates the modern web, where users post and read messages in topic-based newsgroups.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Usenet Target entity description: Usenet is a worldwide distributed discussion system that predates the modern web, where users post and read messages in topic-based newsgroups.
-
A.
NNTP
NNTP (Network News Transfer Protocol) is an application-layer protocol used for reading and posting articles on Usenet newsgroups over TCP/IP networks.
-
B.
Gnus
Gnus is a flexible and extensible message reader for news and email, tightly integrated with the Emacs text editor.
-
C.
Forum
Forum is an independent, avant-garde section of the Berlin International Film Festival that showcases experimental, innovative, and often politically engaged cinema from around the world.
-
D.
sns
sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
-
E.
Foros
Foros is a coastal resort settlement on the southern shore of Crimea, known for its scenic cliffs, beaches, and the landmark Church of the Resurrection overlooking the Black Sea.
- 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_69a8871040f08190aac2e2d0ab6b47ad |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abbb05b51c81908a78c816f492c45c |
completed | March 7, 2026, 5:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae3076afec819091183e328cff58c8 |
completed | March 9, 2026, 2:29 a.m. |
| NEDg | Description generation | batch_69ae30e1c7488190acd6d29c5ad10c33 |
completed | March 9, 2026, 2:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae316398488190b9dd38145d5488b4 |
completed | March 9, 2026, 2:33 a.m. |
Created at: March 4, 2026, 7:43 p.m.