Triple
T29504401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | alpha–beta pruning |
E748469
|
entity |
| Predicate | commonlyAppliedIn |
P11801
|
FINISHED |
| Object | computer chess |
—
|
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: computer chess | Statement: [alpha–beta pruning, commonlyAppliedIn, computer chess]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonlyAppliedIn Context triple: [alpha–beta pruning, commonlyAppliedIn, computer chess]
-
A.
widelyUsedIn
chosen
Indicates that something is commonly or extensively utilized within a particular context, domain, or group.
-
B.
appliedPrimarilyTo
Indicates that something is used mainly or chiefly in relation to a particular target, context, or purpose, rather than being used broadly or equally elsewhere.
-
C.
commonApplication
Indicates that multiple entities share or participate in the same application, process, or usage context.
-
D.
appliesPrimarilyTo
Indicates that a property, rule, or characteristic is mainly relevant or intended for a particular entity or group, more than for others.
-
E.
appliesVia
Indicates that an action, rule, or effect is carried out, implemented, or achieved through a specified method, medium, or mechanism.
- 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_69f0bd455a9c8190b40a3e8ea38cf61f |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69fd37b695c88190855801626f91c4cd |
completed | May 8, 2026, 1:09 a.m. |
| PD | Predicate disambiguation | batch_69fd374cccf08190a230e87164af5938 |
completed | May 8, 2026, 1:07 a.m. |
Created at: April 28, 2026, 4:26 p.m.