Triple
T5923107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ISWIM |
E131742
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | ML |
E131757
|
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: [ISWIM, influenced, ML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ML Context triple: [ISWIM, influenced, ML]
-
A.
ML
chosen
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
ML is the postcode area in central Scotland that covers Motherwell and surrounding towns.
-
C.
ML
ML is a post-nominal honorific indicating a recipient of Papua New Guinea’s Order of Logohu, a national order of merit.
-
D.
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.
-
E.
LFD
LFD is the National Rail station code for Lingfield railway station in Surrey, England.
- 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_69c0085a1ed08190a7e9a8b6323fd680 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03804d9808190829a418adb7864aa |
completed | March 22, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c0483e3481908e50f8b34b11a878 |
completed | March 23, 2026, 4:23 a.m. |
Created at: March 22, 2026, 4 p.m.