Triple
T16795792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Once Upon a Wintertime |
E408226
|
entity |
| Predicate | hasHumanCharacters |
P124663
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Once Upon a Wintertime, hasHumanCharacters, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHumanCharacters Context triple: [Once Upon a Wintertime, hasHumanCharacters, yes]
-
A.
hasCharacters
Indicates that an entity (such as a work or story) includes or features certain characters as part of its content.
-
B.
usesCharacter
Indicates that one entity employs, incorporates, or relies on a particular character (such as a symbol, letter, or persona) in its form, function, or representation.
-
C.
includesNonHumanCharacters
Indicates that the subject contains or features characters that are not human, such as animals, aliens, or other non-human entities.
-
D.
hasCharacterNamedAfter
Indicates that one entity has a character whose name is derived from or intentionally based on another entity.
-
E.
hasIconicCharacter
Indicates that something is associated with a character widely recognized as emblematic or highly representative of it.
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2a96d888190a9876c3784bb7f95 |
completed | April 18, 2026, 4:34 p.m. |
| PD | Predicate disambiguation | batch_69e319cf691c819083e39225f5777ef0 |
completed | April 18, 2026, 5:42 a.m. |
| PDg | Predicate description generation | batch_69e326bac94481908c082117553320f8 |
completed | April 18, 2026, 6:37 a.m. |
Created at: April 10, 2026, 5:22 a.m.