Triple
T15983251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Stuttgart high-speed service |
E387627
|
entity |
| Predicate | usesRollingStock |
P5426
|
FINISHED |
| Object | ICE |
E301224
|
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: ICE | Statement: [Paris–Stuttgart high-speed service, usesRollingStock, ICE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ICE Context triple: [Paris–Stuttgart high-speed service, usesRollingStock, ICE]
-
A.
ICE
ICE is a U.S. federal agency under the Department of Homeland Security responsible for enforcing immigration laws and investigating customs, border, and national security-related offenses.
-
B.
ICE
ICE is the stock ticker symbol for Intercontinental Exchange, a major global operator of financial exchanges and clearing houses.
-
C.
ICE
chosen
ICE is a high-speed international train service operated by Deutsche Bahn that connects major cities across Germany and neighboring countries, including routes through Brussels.
-
D.
ICE
ICE is Emirates’ award-winning in-flight entertainment system offering a wide range of movies, TV, music, and information services to passengers.
-
E.
ICE
ICE is a research institute at Johns Hopkins University focused on advancing the understanding and engineering of cells for biomedical 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15756d6488190ac35da00e96ce21d |
completed | April 16, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3cdf7848190848e9081027dc027 |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:54 a.m.