Triple
T3013865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alicia Christian Foster |
E82285
|
entity |
| Predicate | startedActingCareer |
P45002
|
FINISHED |
| Object | childhood |
—
|
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: childhood | Statement: [Alicia Christian Foster, startedActingCareer, childhood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startedActingCareer Context triple: [Alicia Christian Foster, startedActingCareer, childhood]
-
A.
startedActingOn
Indicates that one entity has begun to perform, execute, or apply an action, influence, or process upon another entity.
-
B.
launchedCareerOf
Indicates that one entity’s actions, support, or involvement initiated or significantly advanced another entity’s professional career.
-
C.
activeYearsInFilm
Indicates the span of years during which an entity was actively involved in film-related work or roles.
-
D.
filmDebut
Indicates the first film in which an entity (typically a person) appeared or participated, marking their initial entry into film work.
-
E.
occupationBegan
Indicates the point in time when an entity started holding a particular occupation or job.
- 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_69ad8b1eb53481908c39bbcd1ec104b2 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a6883c081909b6b74f078e347b7 |
completed | March 8, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69ad961a97188190809dc73430a8eda8 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97ba55dc8190b6dddddfb751cf64 |
completed | March 8, 2026, 3:37 p.m. |
Created at: March 8, 2026, 3 p.m.