Triple
T1786806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meet Me Halfway |
E39410
|
entity |
| Predicate | usesAutoTune |
P32388
|
FINISHED |
| Object | Yes |
—
|
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: Yes | Statement: [Meet Me Halfway, usesAutoTune, Yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesAutoTune Context triple: [Meet Me Halfway, usesAutoTune, Yes]
-
A.
usesModulation
Indicates that one entity applies or employs a particular modulation method or scheme in relation to another entity or process.
-
B.
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.
-
C.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
-
D.
tuning
Indicates the adjustment or calibration of something’s parameters or settings to achieve desired performance or behavior.
-
E.
usesInstrument
Indicates that an agent performs an action by employing a specific instrument or tool as the means to carry it out.
- F. None of above. chosen
Provenance (4 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab75457e54819096b8c6ae8c65550c |
completed | March 7, 2026, 12:45 a.m. |
| PD | Predicate disambiguation | batch_69aa61d165688190924962a98e07ff69 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab75444d28819091c393e62fc97f82 |
completed | March 7, 2026, 12:45 a.m. |
Created at: March 4, 2026, 7:32 p.m.