Triple
T25959371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munna |
E645493
|
entity |
| Predicate | featuresMafia |
P181390
|
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: [Munna, featuresMafia, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresMafia Context triple: [Munna, featuresMafia, yes]
-
A.
featuresSecretSociety
Indicates that a work includes or centers around a secret society as part of its plot or setting.
-
B.
featuresPrivateDetective
Indicates that the subject includes or involves a private detective as a notable element or character.
-
C.
featuresCharacterRole
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
D.
featuresMortal
Indicates that one entity includes, presents, or prominently involves a mortal being as part of its content, composition, or subject matter.
-
E.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
- 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_69e77e85efc08190997da7fcf98bd300 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f7764ab1fc81909f9348db87bd7692 |
completed | May 3, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69f76905d9c88190b1ee810bc9ab644f |
completed | May 3, 2026, 3:25 p.m. |
| PDg | Predicate description generation | batch_69f77648979c8190b6cdbb835ab8987c |
completed | May 3, 2026, 4:22 p.m. |
Created at: April 22, 2026, 8:47 a.m.