Triple
T35298761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cleverbot |
E1019447
|
entity |
| Predicate | approximateScoreInTuringTest |
P199713
|
FINISHED |
| Object | around 59% human-like in some trials |
—
|
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: around 59% human-like in some trials | Statement: [Cleverbot, approximateScoreInTuringTest, around 59% human-like in some trials]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateScoreInTuringTest Context triple: [Cleverbot, approximateScoreInTuringTest, around 59% human-like in some trials]
-
A.
distinctionFromHumans
Indicates a relationship where something is characterized or defined specifically by how it differs from humans.
-
B.
approximateRole
Indicates a relationship where one entity serves in a role that is similar to, but not exactly the same as, the specified role for another entity.
-
C.
typeOfIntelligence
Indicates that one entity is a specific kind or category of intelligence in relation to another entity.
-
D.
evaCount
Indicates a relationship where a specific count or number is associated with an evaluation-related event, action, or occurrence.
-
E.
usesIntelligence
Indicates that an entity applies mental abilities such as reasoning, problem-solving, or understanding to perform an action or achieve a goal.
- 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_69f76de7eedc8190a3bdc64ebbc05b42 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff519b65f081909902ba83b775ef85 |
completed | May 9, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69ff506fccdc8190bd93269589040aed |
completed | May 9, 2026, 3:19 p.m. |
| PDg | Predicate description generation | batch_69ff519a67008190b1eda931fdeff53e |
completed | May 9, 2026, 3:24 p.m. |
Created at: May 3, 2026, 4:03 p.m.