Triple
T271495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MAX Yellow Line |
E5642
|
entity |
| Predicate | hasRollingStockType |
P1305
|
FINISHED |
| Object | Siemens SD660 |
E13599
|
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: Siemens SD660 | Statement: [MAX Yellow Line, hasRollingStockType, Siemens SD660]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siemens SD660 Context triple: [MAX Yellow Line, hasRollingStockType, Siemens SD660]
-
A.
Siemens SD660
chosen
Siemens SD660 is a model of light rail vehicle built by Siemens for use in modern urban transit systems.
-
B.
Siemens S70
The Siemens S70 is a modern low-floor light rail vehicle widely used in North American urban transit systems.
-
C.
Honeywell 316
The Honeywell 316 is a 16-bit minicomputer introduced in the late 1960s, used widely for real-time control, industrial, and embedded applications.
-
D.
Honeywell DDP-516
The Honeywell DDP-516 is a rugged 16-bit minicomputer from the 1960s widely used in early military, industrial, and networking applications, including as a platform for ARPANET Interface Message Processors.
-
E.
Tandberg
Tandberg is a Norwegian company best known for its video conferencing and telepresence solutions, which became part of Cisco Systems after its acquisition.
- 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_69a25853594c8190b05ec3a586ec88bf |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a38f51aeac81908c6d398e650dc315 |
completed | March 1, 2026, 12:58 a.m. |
Created at: Feb. 28, 2026, 2:57 a.m.