Triple
T19060526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kris Aquino |
E466517
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kristina |
—
|
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: Kristina | Statement: [Kris Aquino, givenName, Kristina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristina Context triple: [Kris Aquino, givenName, Kristina]
-
A.
Kristina
chosen
Kristina is a feminine given name commonly used in various European countries, often considered a variant of Christina.
-
B.
Katarina Stenbock
Katarina Stenbock was a Swedish noblewoman who became the third and last wife of King Gustav I of Sweden and served as Queen consort in the 16th century.
-
C.
Ulrike
Ulrike is a German given name, typically feminine, derived from the name Ulrich and associated with German-speaking countries.
-
D.
Kerstin
Kerstin is a feminine given name of Scandinavian origin, particularly common in Sweden and other Nordic countries.
-
E.
Kristina Lugn
Kristina Lugn was a Swedish poet, playwright, and member of the Swedish Academy known for her darkly humorous and psychologically incisive works.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd040fb881909af2a964f65ad208 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5dc0910088190b042095937b202a8 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 10, 2026, 12:03 p.m.