Triple
T11964781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M270 MLRS |
E284764
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object |
Vought
Vought is an American aerospace and defense contractor historically known for producing military aircraft, missiles, and rocket systems.
|
E599046
|
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: Vought | Statement: [M270 MLRS, manufacturer, Vought]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vought Context triple: [M270 MLRS, manufacturer, Vought]
-
A.
Vought
Vought is a surname most notably associated with Russell Vought, an American political figure who served as Director of the U.S. Office of Management and Budget.
-
B.
Vought
Vought was an American aerospace manufacturer best known for producing military aircraft such as the F4U Corsair.
-
C.
Northrop
Northrop is a major American aerospace and defense company known for designing and producing advanced military aircraft and related technologies.
-
D.
Grumman
Grumman was a major American aerospace and defense company best known for designing and building military and naval aircraft, including several iconic U.S. Navy fighters.
-
E.
Folland Aircraft
Folland Aircraft was a British aircraft manufacturer best known for producing light fighter and trainer aircraft in the post-World War II era.
- 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: Vought Triple: [M270 MLRS, manufacturer, Vought]
Generated description
Vought is an American aerospace and defense contractor historically known for producing military aircraft, missiles, and rocket systems.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vought Target entity description: Vought is an American aerospace and defense contractor historically known for producing military aircraft, missiles, and rocket systems.
-
A.
Vought
Vought is a surname most notably associated with Russell Vought, an American political figure who served as Director of the U.S. Office of Management and Budget.
-
B.
Vought
chosen
Vought was an American aerospace manufacturer best known for producing military aircraft such as the F4U Corsair.
-
C.
Northrop
Northrop is a major American aerospace and defense company known for designing and producing advanced military aircraft and related technologies.
-
D.
Grumman
Grumman was a major American aerospace and defense company best known for designing and building military and naval aircraft, including several iconic U.S. Navy fighters.
-
E.
Folland Aircraft
Folland Aircraft was a British aircraft manufacturer best known for producing light fighter and trainer aircraft in the post-World War II era.
- F. None of above.
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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903799f948190a5dc4d3822f3ff27 |
completed | April 10, 2026, 2:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f4594054f08190b28b35f62dfb9198 |
completed | May 1, 2026, 7:41 a.m. |
| NEDg | Description generation | batch_69f45f89d5b08190a87312d96e61898a |
completed | May 1, 2026, 8:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f464a5191881908e291943996169cb |
completed | May 1, 2026, 8:30 a.m. |
Created at: April 8, 2026, 9:46 p.m.