Triple
T12900864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lars Magne Ingebrigtsen |
E308605
|
entity |
| Predicate | softwareProject |
P25429
|
FINISHED |
| Object | Gnus newsreader |
E59588
|
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: Gnus newsreader | Statement: [Lars Magne Ingebrigtsen, softwareProject, Gnus newsreader]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gnus newsreader Context triple: [Lars Magne Ingebrigtsen, softwareProject, Gnus newsreader]
-
A.
Gnus
chosen
Gnus is a flexible and extensible message reader for news and email, tightly integrated with the Emacs text editor.
-
B.
MUTT
MUTT is the common nickname for the M151 MUTT, a lightweight military utility vehicle used extensively by the U.S. armed forces during the Cold War era.
-
C.
NNTP
NNTP (Network News Transfer Protocol) is an application-layer protocol used for reading and posting articles on Usenet newsgroups over TCP/IP networks.
-
D.
Usenet
Usenet is a worldwide distributed discussion system that predates the modern web, where users post and read messages in topic-based newsgroups.
-
E.
NeXT Mail
NeXT Mail was an innovative early email and multimedia messaging application for the NeXTSTEP operating system, notable for pioneering features like integrated rich text and audio attachments.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97180ee708190b60a3e58c42f764f |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af59f3cc81908c99bcde43e724e6 |
completed | May 3, 2026, 2:13 a.m. |
Created at: April 9, 2026, 5:40 p.m.