Triple
T13671260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santos Inocentes |
E327752
|
entity |
| Predicate | hasGreetingOrSaying |
P4600
|
FINISHED |
| Object | ¡Inocente, inocente! |
—
|
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: ¡Inocente, inocente! | Statement: [Santos Inocentes, hasGreetingOrSaying, ¡Inocente, inocente!]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGreetingOrSaying Context triple: [Santos Inocentes, hasGreetingOrSaying, ¡Inocente, inocente!]
-
A.
hasSayingTheme
Indicates that a saying, proverb, or quoted expression is about or centers on a particular theme or subject.
-
B.
hasWordForHello
Indicates that a language or entity possesses a specific word or expression used to say "hello" or greet.
-
C.
typicalGreeting
chosen
Indicates the standard or commonly used way one entity greets another in a given context.
-
D.
includesSaying
Indicates that one entity (such as a text, speech, or communication) contains or incorporates a particular saying, phrase, or quoted expression.
-
E.
hasSpeech
Indicates that an entity produces, delivers, or is associated with a spoken utterance or verbal expression.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc6599c248190b7f134b5b9947a23 |
completed | April 12, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69dbbe8d8d0881908d6e89954f44eed4 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:53 p.m.