Triple
T6540618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D. Ramanaidu |
E168275
|
entity |
| Predicate | numberOfFilmsProduced |
P72282
|
FINISHED |
| Object | over 130 |
—
|
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: over 130 | Statement: [D. Ramanaidu, numberOfFilmsProduced, over 130]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFilmsProduced Context triple: [D. Ramanaidu, numberOfFilmsProduced, over 130]
-
A.
numberOfFilmsDirected
Indicates the total count of films that a given entity has directed.
-
B.
numberOfFilmsAppearedIn
Indicates the total count of distinct films in which a given entity has appeared.
-
C.
producedFilm
Indicates that one entity served as the producer (or production company) responsible for making or financing the creation of a particular film.
-
D.
producedFilmSeries
Indicates that a person or organization served as a producer for a particular film series.
-
E.
producedFilmStarring
Indicates that a person or company produced a film in which a specified actor or set of actors starred.
- 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_69c68a51564081909e93aee0dbd9cca3 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ce07332481909a5a7964282eb776 |
completed | March 27, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69c6acf3e3708190b052ec774e607cb7 |
completed | March 27, 2026, 4:14 p.m. |
| PDg | Predicate description generation | batch_69c6ce0538f48190abf3160681901c17 |
completed | March 27, 2026, 6:35 p.m. |
Created at: March 27, 2026, 1:50 p.m.