Triple
T10488108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CAF Urbos 3 |
E247343
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object | CAF |
E59649
|
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: CAF | Statement: [CAF Urbos 3, manufacturer, CAF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CAF Context triple: [CAF Urbos 3, manufacturer, CAF]
-
A.
CAF
CAF is the Confederation of African Football, the governing body for association football in Africa and one of FIFA’s six continental confederations.
-
B.
CAF
chosen
CAF is a Spanish multinational company that designs and manufactures railway vehicles and related transport equipment used by metro systems worldwide.
-
C.
CAF
CAF is an abbreviation that can refer to various organizations or groups, most notably the Cactus Air Force, a World War II Allied air unit based on Guadalcanal.
-
D.
CAF
CAF is the commonly used abbreviation for the Chief of Air Force, the professional head of an air force service.
-
E.
CAF
CAF is a multilateral development bank that finances sustainable development and regional integration projects across Latin America and the Caribbean.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5096b609c81909e23fd8fb6426f4a |
completed | April 7, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dc8c73748190b97c78af6cf142d9 |
completed | April 10, 2026, 11:18 a.m. |
Created at: April 6, 2026, 12:23 p.m.