Triple
T10267750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chief of Naval Education and Training |
E240752
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
CNET
CNET is a United States Navy command responsible for overseeing education and training programs for naval personnel.
|
E853055
|
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: CNET | Statement: [Chief of Naval Education and Training, shortName, CNET]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CNET Context triple: [Chief of Naval Education and Training, shortName, CNET]
-
A.
Engadget
Engadget is a technology news and reviews website that covers consumer electronics, gadgets, and digital culture.
-
B.
PC World
PC World is a long-running computer and technology magazine known for its reviews, news, and analysis of consumer tech products and trends.
-
C.
Computerworld
Computerworld is a long-running information technology magazine and online publication focused on news, analysis, and insights for IT professionals and business technology leaders.
-
D.
Gizmodo
Gizmodo is a technology and design-focused news and opinion website known for its coverage of gadgets, science, and digital culture.
-
E.
PC Zone
PC Zone was a British computer and video games magazine known for its irreverent humor, critical reviews, and influential commentary on PC gaming.
- 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: CNET Triple: [Chief of Naval Education and Training, shortName, CNET]
Generated description
CNET is a United States Navy command responsible for overseeing education and training programs for naval personnel.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CNET Target entity description: CNET is a United States Navy command responsible for overseeing education and training programs for naval personnel.
-
A.
Engadget
Engadget is a technology news and reviews website that covers consumer electronics, gadgets, and digital culture.
-
B.
PC World
PC World is a long-running computer and technology magazine known for its reviews, news, and analysis of consumer tech products and trends.
-
C.
Computerworld
Computerworld is a long-running information technology magazine and online publication focused on news, analysis, and insights for IT professionals and business technology leaders.
-
D.
Gizmodo
Gizmodo is a technology and design-focused news and opinion website known for its coverage of gadgets, science, and digital culture.
-
E.
PC Zone
PC Zone was a British computer and video games magazine known for its irreverent humor, critical reviews, and influential commentary on PC gaming.
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d26df80081908514fd5c9392e2b7 |
completed | April 7, 2026, 9:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f805d49c8190becddbbf17ac65fd |
completed | April 9, 2026, 12:51 a.m. |
| NEDg | Description generation | batch_69d6fcaca55c81908a48ac2a0ce24b85 |
completed | April 9, 2026, 1:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6fd772bc08190bf270f5fc767fb29 |
completed | April 9, 2026, 1:14 a.m. |
Created at: April 6, 2026, 11:34 a.m.