Triple
T1517724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pepa |
E32157
|
entity |
| Predicate | tuning |
P29977
|
FINISHED |
| Object | not standardized |
—
|
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: not standardized | Statement: [Pepa, tuning, not standardized]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tuning Context triple: [Pepa, tuning, not standardized]
-
A.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
-
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.
tone
Indicates the characteristic attitude or emotional quality expressed in how something is communicated or presented.
-
D.
track
Indicates that one entity follows, monitors, or keeps a record of another entity’s state, behavior, or progress over time.
-
E.
usesModulation
Indicates that one entity applies or employs a particular modulation method or scheme in relation to another entity or process.
- 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_69a885e8caf88190a5fbb6159ce87786 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9396e16408190b5e7b0ac43376d81 |
completed | March 5, 2026, 8:06 a.m. |
| PD | Predicate disambiguation | batch_69a907aa67cc81909f00135365447399 |
completed | March 5, 2026, 4:33 a.m. |
| PDg | Predicate description generation | batch_69a9396d3e1081909aa269b42041c357 |
completed | March 5, 2026, 8:06 a.m. |
Created at: March 4, 2026, 7:26 p.m.