Triple
T4568781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Morning |
E121977
|
entity |
| Predicate | firstAppearanceFilmYear |
P18082
|
FINISHED |
| Object | 1939 |
—
|
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: 1939 | Statement: [Good Morning, firstAppearanceFilmYear, 1939]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstAppearanceFilmYear Context triple: [Good Morning, firstAppearanceFilmYear, 1939]
-
A.
firstAppearanceFor
Indicates that an entity marks the initial occurrence or debut of another entity within a given context or medium.
-
B.
firstAppearanceEpisode
Indicates the specific episode in which an entity (such as a character or item) is shown or mentioned for the first time.
-
C.
firstAppeared
Indicates the earliest known time or context in which an entity was introduced, observed, or came into existence.
-
D.
characterFirstAppearanceInThisPortrayal
Indicates that this is the first time the character appears in the specific portrayal or adaptation being referenced.
-
E.
yearOfFilmAppearance
chosen
Indicates the specific year in which a film appearance by an entity took place.
- F. None of above.
Provenance (3 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_69bd463f156881908a99aca69c5721ac |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd58a0dfbc81909b5023f0c29addaf |
completed | March 20, 2026, 2:24 p.m. |
| PD | Predicate disambiguation | batch_69bd5227063c8190973155a875b013a7 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:10 p.m.