Triple
T10560528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AMX-10P |
E249205
|
entity |
| Predicate | designedBy |
P184
|
FINISHED |
| Object | GIAT Industries |
E207027
|
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: GIAT Industries | Statement: [AMX-10P, designedBy, GIAT Industries]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GIAT Industries Context triple: [AMX-10P, designedBy, GIAT Industries]
-
A.
GIAT Industries
chosen
GIAT Industries is a French state-owned defense company known for designing and producing military equipment, including small arms, armored vehicles, and artillery systems.
-
B.
Shao Industries
Shao Industries is a company or corporate entity associated with and employing Liwen Shao.
-
C.
Litton Industries
Litton Industries was a major American conglomerate best known for its diversified operations in electronics, defense, and industrial manufacturing during the mid-20th century.
-
D.
Guardian Industries
Guardian Industries is a major global manufacturer of glass, automotive, and building products, known for its architectural and float glass operations.
-
E.
Kaiser Industries
Kaiser Industries was a diversified American industrial conglomerate founded by Henry J. Kaiser, active in sectors such as construction, shipbuilding, aluminum, steel, and automotive manufacturing.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5271f3c6c819080b49fbe3aa09e09 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b38b4c081908cc2816144c23152 |
completed | April 10, 2026, 7:10 p.m. |
Created at: April 6, 2026, 12:35 p.m.