Triple
T9269909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Talgo 350 |
E222797
|
entity |
| Predicate | safetySystem |
P840
|
FINISHED |
| Object | ASFA |
E582705
|
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: ASFA | Statement: [Talgo 350, safetySystem, ASFA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ASFA Context triple: [Talgo 350, safetySystem, ASFA]
-
A.
ASFA
chosen
ASFA is a Spanish railway automatic train protection system designed to monitor and control train speeds to enhance operational safety.
-
B.
AFSA
AFSA was a U.S. military signals intelligence and cryptologic organization that served as a predecessor to the National Security Agency (NSA).
-
C.
AFAS
AFAS is a regional agreement among ASEAN member states aimed at progressively liberalizing trade in services to enhance economic integration and competitiveness in Southeast Asia.
-
D.
ASA
ASA is the commonly used abbreviation for the Academy of Sciences of Albania, the country’s leading scientific research and advisory institution.
-
E.
ASA
ASA is the leading professional organization in the United States dedicated to advancing the practice and profession of statistics.
- 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_69ca841ffe208190aa7bcffbef2f8379 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd078525308190abfb883123742d3f |
completed | April 1, 2026, 11:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d09c2239a08190b954c8c57ced8fd2 |
completed | April 4, 2026, 5:05 a.m. |
Created at: March 30, 2026, 7:33 p.m.