Triple
T9994861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Hemsworth |
E197177
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Elsa Pataky |
E668112
|
NE 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: Elsa Pataky | Statement: [Chris Hemsworth, spouse, Elsa Pataky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elsa Pataky Context triple: [Chris Hemsworth, spouse, Elsa Pataky]
-
A.
Elsa Pataky
chosen
Elsa Pataky is a Spanish actress and model best known for her roles in the Fast & Furious film franchise and various international action and thriller movies.
-
B.
Elsa Martinelli
Elsa Martinelli was an Italian actress and fashion model known for her international film career in the 1950s and 1960s.
-
C.
Elena Anaya
Elena Anaya is a Spanish actress known for her roles in both European cinema and Hollywood productions, including prominent performances in films like "The Skin I Live In."
-
D.
Bérénice Marlohe
Bérénice Marlohe is a French actress best known internationally for her role as Sévérine in the James Bond film "Skyfall."
-
E.
Ana de Armas
Ana de Armas is a Cuban-Spanish actress known for her breakout roles in films such as "Blade Runner 2049," "Knives Out," and "Blonde."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcb99ac74819091f20816478ea375 |
completed | April 2, 2026, 1:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d258336ab8819098d4878b8c106d86 |
completed | April 5, 2026, 12:40 p.m. |
Created at: March 30, 2026, 8:50 p.m.