Triple
T27203210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Best Short Film |
E683791
|
entity |
| Predicate | oftenDividedInto |
P82863
|
FINISHED |
| Object | live action short film |
—
|
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: live action short film | Statement: [Best Short Film, oftenDividedInto, live action short film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenDividedInto Context triple: [Best Short Film, oftenDividedInto, live action short film]
-
A.
sometimesDividedInto
chosen
Indicates that an entity is on some occasions partitioned or separated into distinct parts, sections, or groups, but not always.
-
B.
dividedIn
Indicates that one entity is partitioned or separated into multiple distinct parts, sections, or groups represented by another entity.
-
C.
dividedBetween
Indicates that something is partitioned or shared among two or more distinct entities or groups.
-
D.
wasDividedBetween
Indicates that something was partitioned into portions that were allocated to two or more distinct recipients or groups.
-
E.
traditionallyDividedInto
Indicates that something is customarily or historically separated into specific parts, sections, or categories according to established tradition.
- 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_69eefad1fd5c8190a4a46ea6afe58bfa |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f6bbf6e33c819086e5176d64e7a614 |
completed | May 3, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6b1e6c8190adf9d6a257e0b744 |
completed | May 3, 2026, 3 a.m. |
Created at: April 27, 2026, 9:37 a.m.