Triple
T15264600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nimbus |
E364865
|
entity |
| Predicate | writtenIn |
P12727
|
FINISHED |
| Object | Nim |
E1147463
|
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: Nim | Statement: [Nimbus, writtenIn, Nim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nim Context triple: [Nimbus, writtenIn, Nim]
-
A.
Nim
Nim is a classic impartial combinatorial game of removing objects from heaps, fundamental in game theory and central to the development of the Sprague–Grundy theorem.
-
B.
Nim
chosen
Nim is a statically typed, compiled programming language that emphasizes performance, expressiveness, and a Python-like syntax while targeting C, C++, and JavaScript backends.
-
C.
NIM
NIM is the IATA airport code for Diori Hamani International Airport, the main airport serving Niamey, the capital of Niger.
-
D.
Wythoff Nim
Wythoff Nim is a classic impartial combinatorial game involving two piles of tokens, whose optimal play is characterized by positions related to the golden ratio.
-
E.
Kayles
Kayles is a classic impartial combinatorial game in which players alternately remove one or two adjacent pins from a row, with the goal of making the last move.
- 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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0084fed0481908e452c89cba2be82 |
completed | April 15, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef6fc290819096ef03f8ecae8876 |
completed | May 9, 2026, 8:25 a.m. |
Created at: April 10, 2026, 3:14 a.m.