Triple
T23683734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mama (TV series) |
E585103
|
entity |
| Predicate | adaptationFromMedium |
P153371
|
FINISHED |
| Object | theatre |
—
|
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: theatre | Statement: [Mama (TV series), adaptationFromMedium, theatre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adaptationFromMedium Context triple: [Mama (TV series), adaptationFromMedium, theatre]
-
A.
adaptationBy
Indicates a relationship where one entity has been modified, transformed, or reworked by another entity into a new form or version.
-
B.
adaptedAs
Indicates that one work, concept, or entity has been transformed or re-created into another form or medium based on the original.
-
C.
adaptationOfWorkGenre
Indicates that one work is an adaptation of another work that belongs to a specific genre.
-
D.
mediumAdaptation
Indicates that one work has been adapted into another form or medium (e.g., book to film, comic to TV series).
-
E.
hasAdaptedMedium
Indicates that an original work has been transformed or adapted into a different medium or format (e.g., book to film, comic to TV series).
- 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_69e24901f7c08190909fd727632e823d |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b4fa72b48190b872670b6546a718 |
completed | April 29, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f155d5265881908e43a9696b6a6d0f |
completed | April 29, 2026, 12:50 a.m. |
| PDg | Predicate description generation | batch_69f157cc43a881909ed2d8b0a09b5d73 |
completed | April 29, 2026, 12:58 a.m. |
Created at: April 17, 2026, 6:52 p.m.