Triple
T24251670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | María García |
E603542
|
entity |
| Predicate | sharesGivenNameWithMany |
P29733
|
FINISHED |
| Object | María is one of the most frequent Spanish female given names |
—
|
NE NERFINISHED |
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: María is one of the most frequent Spanish female given names | Statement: [María García, sharesGivenNameWithMany, María is one of the most frequent Spanish female given names]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesGivenNameWithMany Context triple: [María García, sharesGivenNameWithMany, María is one of the most frequent Spanish female given names]
-
A.
sharesGivenNameWith
Indicates that two entities have the same given (first) name.
-
B.
sharesNameWithMultiplePeople
chosen
Indicates that an entity has the same name as more than one distinct other entity.
-
C.
sharesNameWith
Indicates that two entities have the same name or an identical naming designation.
-
D.
sharesNameRootWith
Indicates that two entities have names derived from the same linguistic root or base form.
-
E.
sharesUniverseWith
Indicates that two entities exist within the same fictional or narrative universe, implying shared continuity, setting, or canon.
- 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_69e29540da0481909a38bdae315b7a02 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28b89eb8081909ccf37081add5cee |
completed | April 29, 2026, 10:51 p.m. |
| PD | Predicate disambiguation | batch_69f1c450aa508190bc9d372a5f6ee47a |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, 12:05 a.m.