Triple
T29504414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | alpha–beta pruning |
E748469
|
entity |
| Predicate | prunes |
P9925
|
FINISHED |
| Object | subtrees that cannot improve current best outcome |
—
|
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: subtrees that cannot improve current best outcome | Statement: [alpha–beta pruning, prunes, subtrees that cannot improve current best outcome]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: prunes Context triple: [alpha–beta pruning, prunes, subtrees that cannot improve current best outcome]
-
A.
pruningTime
Indicates the time or period during which a pruning action is performed on something.
-
B.
pruningType
Indicates the specific method or style of pruning applied to an entity (e.g., how it is cut back or trimmed).
-
C.
reduces
chosen
Indicates that one entity causes a decrease in the amount, intensity, degree, or impact of another entity.
-
D.
pruningTolerance
Indicates how well an entity can withstand or recover from being cut back, trimmed, or pruned.
-
E.
tramples
Indicates that one entity crushes, damages, or overrides another by forcefully stepping or pressing down on it, often implying dominance or disregard.
- 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_69f66c35c9608190937a51b5de166390 |
completed | May 2, 2026, 9:27 p.m. |
| PD | Predicate disambiguation | batch_69f6659b62fc8190b21555d0ba54db2d |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 28, 2026, 4:26 p.m.