Triple
T6356376
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FC Lausanne-Sport |
E143001
|
entity |
| Predicate | owner |
P347
|
FINISHED |
| Object | Ineos |
E186565
|
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: Ineos | Statement: [FC Lausanne-Sport, owner, Ineos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ineos Context triple: [FC Lausanne-Sport, owner, Ineos]
-
A.
Ineos
chosen
Ineos is a large multinational chemicals and energy company based in the United Kingdom, known for its extensive portfolio of petrochemical, oil, gas, and manufacturing operations worldwide.
-
B.
BASF
BASF is a major German chemical company and one of the world's largest producers of chemicals and related products.
-
C.
Arkema
Arkema is a French multinational specialty chemicals and advanced materials company known for its innovations in adhesives, coatings, and performance polymers.
-
D.
SABIC
SABIC is a major Saudi-based global petrochemicals and plastics manufacturer known as one of the world’s largest chemical companies.
-
E.
Celanese Corporation
Celanese Corporation is a global specialty materials and chemical company known for producing engineered materials, acetyl products, and other advanced polymers for industrial and consumer applications.
- 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067f37d0881909289bafb09e29298 |
completed | March 22, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d5966a88190a7184157b235b170 |
completed | March 27, 2026, 7:10 a.m. |
Created at: March 22, 2026, 4:32 p.m.