Triple
T2939267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R46 subway cars |
E79343
|
entity |
| Predicate | linesServed |
P6301
|
FINISHED |
| Object | C |
E192527
|
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: C | Statement: [R46 subway cars, linesServed, C]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: C Context triple: [R46 subway cars, linesServed, C]
-
A.
C
C is a foundational, general-purpose programming language known for its efficiency, low-level memory access, and influence on many later languages such as C++, Java, and Python.
-
B.
C
chosen
C is a local service on the New York City Subway that runs along the Eighth Avenue Line in Manhattan and continues through Brooklyn.
-
C.
CPP
CPP is a Canadian government-run public pension program that provides retirement, disability, and survivor benefits to eligible contributors.
-
D.
CPP
CPP is a public polytechnic university in Pomona, California, known for its hands-on, learn-by-doing educational approach.
-
E.
.cc
.cc is the country code top-level domain (ccTLD) assigned to the Cocos (Keeling) Islands, often marketed globally for a variety of commercial and creative uses.
- 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_69ad8b0fbab081908f6a61567c045d8d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9e0fec048190bdd70c60ec5c92cd |
completed | March 8, 2026, 4:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b08686b0388190a214ad8a615f2da5 |
completed | March 10, 2026, 9 p.m. |
Created at: March 8, 2026, 2:56 p.m.