Triple
T14894936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telenet Group |
E359846
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | YUGO |
E1121510
|
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: YUGO | Statement: [Telenet Group, brand, YUGO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: YUGO Context triple: [Telenet Group, brand, YUGO]
-
A.
Zastava Automobiles
chosen
Zastava Automobiles is a Serbian car manufacturer best known internationally for producing the Yugo and other affordable compact vehicles during the Yugoslav era.
-
B.
Yugo Amaryl
Yugo Amaryl is a brilliant mathematician in Isaac Asimov’s Foundation series who becomes Hari Seldon’s closest collaborator in developing the science of psychohistory.
-
C.
Lada
Lada is a Slavic goddess commonly associated with love, beauty, fertility, and the renewal of nature.
-
D.
Avtovo
Avtovo is a renowned Saint Petersburg Metro station celebrated for its ornate, palace-like interior and distinctive Soviet-era architectural design.
-
E.
Biqueli
Biqueli is a small coastal settlement on Atauro Island in East Timor, known for its fishing community and proximity to coral reefs.
- 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded6070b248190be8f4f91a0c0b1f3 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b679fb081908cf8f41acfba3b99 |
completed | May 8, 2026, 11:01 p.m. |
Created at: April 10, 2026, 2:10 a.m.