Triple
T8330387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christmas stories |
E195058
|
entity |
| Predicate | oftenReadDuring |
P82140
|
FINISHED |
| Object | Advent |
—
|
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: Advent | Statement: [Christmas stories, oftenReadDuring, Advent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenReadDuring Context triple: [Christmas stories, oftenReadDuring, Advent]
-
A.
oftenSoughtOn
Indicates that one entity is frequently searched for, requested, or pursued in relation to another entity.
-
B.
oftenUsedAfter
Indicates that one entity is frequently or typically used immediately following another entity in a sequence or workflow.
-
C.
oftenSays
Indicates that one entity frequently makes a particular statement or remark, or regularly expresses a certain idea or phrase.
-
D.
oftenHeldToBe
Indicates that something is frequently regarded, considered, or believed to be a certain way by many people or in many contexts.
-
E.
frequentOccasion
Indicates that a particular event, situation, or condition occurs repeatedly or commonly over time.
- 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_69ca82e87f2c8190bdb71ee29dfc642d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fb995508190b2ca94ad45bf6d24 |
completed | March 31, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69cb70c3231c81909e3d463192c9de22 |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb76d823b08190a54fadb50660cda5 |
completed | March 31, 2026, 7:25 a.m. |
Created at: March 30, 2026, 5:56 p.m.