Triple
T13775990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RFC 1031 |
E331007
|
entity |
| Predicate | networkContext |
P25808
|
FINISHED |
| Object | MILNET |
E23856
|
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: MILNET | Statement: [RFC 1031, networkContext, MILNET]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MILNET Context triple: [RFC 1031, networkContext, MILNET]
-
A.
MILNET
chosen
MILNET was a U.S. military computer network that formed the unclassified, operational branch of the early Defense Data Network, separate from research-focused ARPANET.
-
B.
MIL
MIL is the standard abbreviation used for the Milwaukee Admirals, a professional ice hockey team based in Milwaukee, Wisconsin.
-
C.
MIL
MIL is the vehicle registration code used for cars registered in the Miltenberg district of Bavaria, Germany.
-
D.
NIPRNET
NIPRNET is the U.S. Department of Defense’s primary unclassified but sensitive IP-based network used for day-to-day administrative and operational communications.
-
E.
Nitelink
Nitelink is Dublin’s late-night bus service network, providing after-hours public transport on key routes across the city and suburbs.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0238bdbc8190a946e6e5431632a5 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a86afd788190ab637044dd489a24 |
completed | May 3, 2026, 7:56 p.m. |
Created at: April 9, 2026, 10:10 p.m.