Triple
T62799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christmas |
E1245
|
entity |
| Predicate | typicalGreeting |
P4600
|
FINISHED |
| Object | Merry Christmas |
—
|
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: Merry Christmas | Statement: [Christmas, typicalGreeting, Merry Christmas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalGreeting Context triple: [Christmas, typicalGreeting, Merry Christmas]
-
A.
typicalSchedule
Indicates the usual or standard timing and sequence of activities or events associated with an entity.
-
B.
typicalSpeaker
Indicates that the subject is a prototypical or characteristic speaker or source of utterances in the context of the object.
-
C.
typicalActivity
Indicates that an entity is commonly or characteristically engaged in a particular activity.
-
D.
typicalFlavor
Indicates that something characteristically has or is associated with a particular flavor.
-
E.
openingLine
Indicates that one entity is the first line or initial statement that begins another entity, such as a text, speech, or conversation.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a251f74b0881909ad89127b8171277 |
completed | Feb. 28, 2026, 2:24 a.m. |
| PD | Predicate disambiguation | batch_69a24ea242c8819086fe00bf01e6523e |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a251f6786081908eaaed6190695322 |
completed | Feb. 28, 2026, 2:24 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.