Triple
T1685027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BMT Jamaica Line |
E36422
|
entity |
| Predicate | hasService |
P182
|
FINISHED |
| Object | M service |
E187451
|
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: M service | Statement: [BMT Jamaica Line, hasService, M service]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: M service Context triple: [BMT Jamaica Line, hasService, M service]
-
A.
M
M is a functional data mashup and query language used in Microsoft Power BI and related tools for data transformation and preparation.
-
B.
M
M is the codename for James Bond’s stern and authoritative superior who heads the British Secret Service in the 007 franchise.
-
C.
M
"M" is a 1951 American crime thriller film directed by Joseph Losey, adapted from Fritz Lang’s 1931 classic, in which David Wayne portrays a hunted child murderer.
-
D.
M
chosen
M is a New York City Subway service that runs along the IND Sixth Avenue Line in Manhattan and connects Brooklyn and Queens.
-
E.
SVC
SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
- 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa627da0688190bfb5316079bc589a |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad71c1b4308190b04fed7ce752b67c |
completed | March 8, 2026, 12:55 p.m. |
Created at: March 4, 2026, 7:29 p.m.