Triple
T2592878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District 2 (Caltrans) |
E58162
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | D2 |
E250700
|
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: D2 | Statement: [District 2 (Caltrans), hasAbbreviation, D2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: D2 Context triple: [District 2 (Caltrans), hasAbbreviation, D2]
-
A.
D2
chosen
D2 is a line of the Moscow Central Diameters suburban rail system, providing cross-city commuter rail service through Moscow and its surrounding areas.
-
B.
D-2
D-2 was a 1993 German-led Spacelab space shuttle mission focused on microgravity and life sciences research in low Earth orbit.
-
C.
D4
D4 is a commuter rail line within Moscow’s Moscow Central Diameters network, connecting suburban areas with the city through frequent, urban-style train service.
-
D.
D
D is a statically typed, compiled systems programming language designed as a modern successor to C and C++, emphasizing high performance, safety features, and programmer productivity.
-
E.
D
D is the vehicle registration code used on license plates for the German city of Düsseldorf.
- 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_69ab4ac019c8819094add11c46706e32 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd426e2d4819081a07920b4d2a1cc |
completed | March 7, 2026, 7:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af83bee4908190b5e446ddbf4e8889 |
completed | March 10, 2026, 2:36 a.m. |
Created at: March 6, 2026, 9:49 p.m.