Triple
T10156413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Applied Materials |
E233786
|
entity |
| Predicate | competesWith |
P1375
|
FINISHED |
| Object | ASML |
E352781
|
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: ASML | Statement: [Applied Materials, competesWith, ASML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ASML Context triple: [Applied Materials, competesWith, ASML]
-
A.
ASML
chosen
ASML is a Dutch multinational company that is the world’s leading manufacturer of advanced photolithography equipment used in semiconductor chip production.
-
B.
Asml
Asml is the rail code used to identify Amstelstation in the Dutch railway network.
-
C.
Lam Research
Lam Research is a leading American semiconductor equipment company that supplies wafer fabrication tools and services to chip manufacturers worldwide.
-
D.
TSMC
TSMC (Taiwan Semiconductor Manufacturing Company) is the world’s largest dedicated semiconductor foundry, renowned for producing cutting-edge chips for major technology companies.
-
E.
Sematech
Sematech is a former U.S.-based semiconductor industry consortium that played a key role in advancing chip manufacturing technologies and standards through collaborative research among major chipmakers and government agencies.
- 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_69ca848e80748190b91d1e04d35512c7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec3c47dc81909679903e6024eb49 |
completed | April 2, 2026, 4:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e65b9d4c8190b1f520ed08256372 |
completed | April 5, 2026, 10:46 p.m. |
Created at: March 30, 2026, 9:09 p.m.