Triple
T30338075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houdini (chess engine) |
E771673
|
entity |
| Predicate | hasOptimizationTarget |
P33716
|
FINISHED |
| Object | x86-64 processors |
—
|
LITERAL 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: x86-64 processors | Statement: [Houdini (chess engine), hasOptimizationTarget, x86-64 processors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOptimizationTarget Context triple: [Houdini (chess engine), hasOptimizationTarget, x86-64 processors]
-
A.
canBeOptimizedFor
Indicates that one entity is capable of being improved or adjusted to perform better with respect to another specified criterion, context, or target.
-
B.
optimizationTarget
chosen
Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
-
C.
supportsOptimizationAlgorithm
Indicates that one entity is capable of running, integrating, or being compatible with a specified optimization algorithm.
-
D.
hasTarget
Indicates that one entity is directed toward, aimed at, or intended to affect another specific entity as its target.
-
E.
optimizationLevel
Indicates the degree or intensity to which a process, system, or solution has been refined to improve its performance or efficiency.
- F. None of above.
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_69f2248aba24819095bb86480d55b23b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7465687bc8190a9da44d62b634ed7 |
completed | May 3, 2026, 12:57 p.m. |
| PD | Predicate disambiguation | batch_69f743f4ceb08190a21fe7f4a99b166b |
completed | May 3, 2026, 12:47 p.m. |
Created at: April 29, 2026, 7:54 p.m.