Triple
T1833772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meghalaya |
E41016
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | ML |
E137195
|
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: ML | Statement: [Meghalaya, vehicleRegistrationCode, ML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ML Context triple: [Meghalaya, vehicleRegistrationCode, ML]
-
A.
ML
ML is a statically typed functional programming language developed at the University of Edinburgh, known for pioneering features like type inference, pattern matching, and modules that strongly influenced later languages such as Elm, Haskell, and OCaml.
-
B.
ML
chosen
ML is the postcode area in central Scotland that covers Motherwell and surrounding towns.
-
C.
MS in Machine Learning
MS in Machine Learning is a specialized graduate program at Carnegie Mellon University focused on advanced theory and applications of machine learning and statistical methods for building intelligent systems.
-
D.
LM
LM is the IATA airline designator assigned to Loganair, a regional airline based in Scotland.
-
E.
ML.NET
ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
- 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_69a88647f9388190909bc36e795bdaec |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb02540dc819081b19a09562139cd |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf711c148190ba12d9bc81892095 |
completed | March 8, 2026, 6:26 p.m. |
Created at: March 4, 2026, 7:33 p.m.