Triple
T36628796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Numéro zéro |
E904248
|
entity |
| Predicate | hasMinimalUseOf |
P193750
|
FINISHED |
| Object | editing |
—
|
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: editing | Statement: [Numéro zéro, hasMinimalUseOf, editing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMinimalUseOf Context triple: [Numéro zéro, hasMinimalUseOf, editing]
-
A.
hasMinimalistApproach
Indicates that an entity employs a simple, stripped-down, and non-extravagant method or style in performing an action or fulfilling a function.
-
B.
hasHumanUse
Indicates that something is used, employed, or utilized by humans for a particular purpose or benefit.
-
C.
isReducedDuringUseTo
Indicates that the quantity, intensity, or effectiveness of one entity decreases as a direct result of being used or consumed.
-
D.
hasNotableUseExample
Indicates that there exists a particularly significant or illustrative example of how something is used.
-
E.
usedLessIn
Indicates that one entity is used with a lower frequency or intensity compared to another entity.
- 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_69f76e6ae750819096911e6e2d4d12c5 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd509e6bc08190b263923c2f40fea3 |
completed | May 8, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69fd4fd1a58881909d4b84de1b24e380 |
completed | May 8, 2026, 2:52 a.m. |
| PDg | Predicate description generation | batch_69fd509cdc5c8190a5f2c451bc0d0b25 |
completed | May 8, 2026, 2:55 a.m. |
Created at: May 3, 2026, 4:11 p.m.