Triple
T14930363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Line (Sacramento RT) |
E372244
|
entity |
| Predicate | hasOperatorAbbreviation |
P7409
|
FINISHED |
| Object | SacRT |
E353525
|
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: SacRT | Statement: [Blue Line (Sacramento RT), hasOperatorAbbreviation, SacRT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SacRT Context triple: [Blue Line (Sacramento RT), hasOperatorAbbreviation, SacRT]
-
A.
SacRT
chosen
SacRT is the public transit agency serving the Sacramento, California metropolitan area with bus, light rail, and related transportation services.
-
B.
SacRT Bus
SacRT Bus is the public bus service operated by Sacramento Regional Transit, providing local and regional transportation throughout the Sacramento, California area.
-
C.
SAC
The SAC is the abbreviated name commonly used for the State Affairs Commission, the top governing body in North Korea responsible for major state policy and leadership.
-
D.
SAC
SAC is the company that manages Catania–Fontanarossa Airport, one of the main air transport hubs in Sicily, Italy.
-
E.
SAC
SAC (Soft Actor-Critic) is a popular off-policy deep reinforcement learning algorithm that optimizes both expected return and policy entropy to achieve stable and efficient learning in continuous control tasks.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded64550dc8190ba44120df00ba498 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72c728948190a1cfc62f4038b6ec |
completed | May 8, 2026, 11:33 p.m. |
Created at: April 10, 2026, 2:36 a.m.