Triple
T7385089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zoomlion |
E170359
|
entity |
| Predicate | competitor |
P1375
|
FINISHED |
| Object | Sany |
E170358
|
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: Sany | Statement: [Zoomlion, competitor, Sany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sany Context triple: [Zoomlion, competitor, Sany]
-
A.
Sany Heavy Industry
chosen
Sany Heavy Industry is a major Chinese multinational heavy equipment manufacturer known for its construction machinery, cranes, and concrete machinery.
-
B.
XCMG
XCMG is a major Chinese construction machinery manufacturer and one of the world’s largest producers of heavy equipment.
-
C.
Japanese crane company Tadano
Japanese crane company Tadano is a leading global manufacturer of lifting equipment and mobile cranes, known for supplying heavy machinery to major construction and restoration projects worldwide.
-
D.
Hyundai Construction Equipment
Hyundai Construction Equipment is a global manufacturer of construction machinery and equipment, known for its excavators, wheel loaders, and industrial vehicles used in infrastructure and industrial projects.
-
E.
Hino Motors, Ltd.
Hino Motors, Ltd. is a Japanese manufacturer specializing in commercial vehicles and diesel engines, known particularly for its trucks and buses.
- 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_69c68a5d0ed08190b6d361e68f813330 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1efe1308190b96eefbff56140be |
completed | March 27, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8276ec3b88190b720354787f7a735 |
completed | March 28, 2026, 7:09 p.m. |
Created at: March 27, 2026, 3:08 p.m.