Triple
T24058077
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malinda Maynor Lowery |
E595863
|
entity |
| Predicate | hasMadeFilm |
P154699
|
FINISHED |
| Object | In the Light of Reverence |
—
|
NE NERFINISHED |
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: In the Light of Reverence | Statement: [Malinda Maynor Lowery, hasMadeFilm, In the Light of Reverence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMadeFilm Context triple: [Malinda Maynor Lowery, hasMadeFilm, In the Light of Reverence]
-
A.
hasNotableFilm
Indicates that an entity is associated with a film that is considered significant, well-known, or particularly noteworthy.
-
B.
hasLiveActionFilm
Indicates that a subject has a corresponding live-action film adaptation or representation.
-
C.
hasDirectedFeatureFilm
Indicates that a person has directed a particular feature-length film.
-
D.
hasFirstFilm
Indicates the specific film that is recognized as the first film associated with an entity (such as a person, series, or franchise).
-
E.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
- 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_69e288c184b081909f1f1751fb8e299a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1da52b9d48190b503fad5ff70e4c6 |
completed | April 29, 2026, 10:15 a.m. |
| PD | Predicate disambiguation | batch_69f1764b1d4c8190b12590c6339c31c1 |
completed | April 29, 2026, 3:08 a.m. |
| PDg | Predicate description generation | batch_69f1785afe3c81909be28986ffe944bf |
completed | April 29, 2026, 3:17 a.m. |
Created at: April 17, 2026, 10:35 p.m.