Triple
T14538414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | G1 GC |
E341109
|
entity |
| Predicate | hasTuningOption |
P29977
|
FINISHED |
| Object | -XX:+UseG1GC |
—
|
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: -XX:+UseG1GC | Statement: [G1 GC, hasTuningOption, -XX:+UseG1GC]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTuningOption Context triple: [G1 GC, hasTuningOption, -XX:+UseG1GC]
-
A.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
-
B.
supportsWeaponTuning
Indicates that an entity provides the capability to adjust, modify, or customize the characteristics or performance of a weapon.
-
C.
isTunedTo
Indicates that one entity has been adjusted or configured to operate at, receive, or correspond to the frequency, channel, or setting of another entity.
-
D.
tuning
chosen
Indicates the adjustment or calibration of something’s parameters or settings to achieve desired performance or behavior.
-
E.
usesAutoTune
Indicates that the subject employs automatic pitch-correction technology (Auto-Tune) on their vocal or audio recordings.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb1bb90008190947ac0961393446d |
completed | April 14, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69de5c546c7081909e27d504ec360c5c |
completed | April 14, 2026, 3:25 p.m. |
Created at: April 10, 2026, 1:22 a.m.