Triple
T256961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marie Mosquini |
E5455
|
entity |
| Predicate | numberOfFilmsAppearedIn |
P8980
|
FINISHED |
| Object | 140+ |
—
|
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: 140+ | Statement: [Marie Mosquini, numberOfFilmsAppearedIn, 140+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFilmsAppearedIn Context triple: [Marie Mosquini, numberOfFilmsAppearedIn, 140+]
-
A.
playedRoleIn
Indicates that an entity performed or assumed a specific role or character within a particular event, production, or context.
-
B.
appearsIn
Indicates that an entity is present, featured, or occurs within a particular context, work, or medium.
-
C.
starredActor
Indicates that an actor performed a leading or significant role in a particular production or work.
-
D.
playedKeyRoleIn
Indicates that an entity had a major, influential, or decisive impact on the occurrence, outcome, or success of another entity or event.
-
E.
filmSeries
Indicates that a film is part of, or associated with, a larger film series or franchise.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25d5884c88190a349d7593b688921 |
completed | Feb. 28, 2026, 3:13 a.m. |
| PD | Predicate disambiguation | batch_69a25b694c08819085bb4b256fa7736f |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25c4b773c81908f1017f40b0bfd07 |
completed | Feb. 28, 2026, 3:08 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.