Triple
T9243931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Little Giant |
E222137
|
entity |
| Predicate | madeActressA |
P87752
|
FINISHED |
| Object | major star of the 1920s |
—
|
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: major star of the 1920s | Statement: [The Little Giant, madeActressA, major star of the 1920s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: madeActressA Context triple: [The Little Giant, madeActressA, major star of the 1920s]
-
A.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
B.
madeFamousByFilm
Indicates that something became widely known or gained significant public recognition as a result of being featured in a film.
-
C.
majorActor
Indicates that the subject is a primary or leading participant in the action, event, or production involving the object.
-
D.
startedActingCareer
Indicates that an entity began their professional work or involvement in acting at a specific time or event.
-
E.
MarilynMonroeRoleType
Indicates the type or category of role associated with Marilyn Monroe in a given context.
- 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_69ca83ee26cc81909ac624e190597d6d |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cd03edd37481908ea2f6dac354f04f |
completed | April 1, 2026, 11:39 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4765648190aa9445c4a22dc471 |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc95597be081908ece2491dd2f0f74 |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:30 p.m.