Triple
T113127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosh Hashanah |
E2287
|
entity |
| Predicate | greetingMeaning |
P4600
|
FINISHED |
| Object | Have a good year |
—
|
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: Have a good year | Statement: [Rosh Hashanah, greetingMeaning, Have a good year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: greetingMeaning Context triple: [Rosh Hashanah, greetingMeaning, Have a good year]
-
A.
typicalGreeting
chosen
Indicates the standard or commonly used way one entity greets another in a given context.
-
B.
titleMeaning
Indicates that one entity expresses or explains the meaning, significance, or interpretation of another entity’s title.
-
C.
meaningOfNativeName
Indicates that one entity specifies the semantic meaning or translation of another entity’s native-language name.
-
D.
translationOfMotto
Indicates that one motto is a translation of another motto in a different language.
-
E.
hasLiteralMeaning
Indicates that one entity expresses the direct, explicit meaning or sense of another entity (such as a word, phrase, or symbol).
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a258808ff08190a06b6206f635612b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256425a488190959d71e39e699d90 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.