Triple
T16253645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Call Jane |
E394574
|
entity |
| Predicate | portraysCharacter |
P1668
|
FINISHED |
| Object | Kate Mara as Lana |
E346443
|
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: Kate Mara as Lana | Statement: [Call Jane, portraysCharacter, Kate Mara as Lana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Mara as Lana Context triple: [Call Jane, portraysCharacter, Kate Mara as Lana]
-
A.
Lana
Lana is a river in Albania that flows through the capital city of Tirana before joining the Tirana River.
-
B.
Lana
Lana is the ISO 15924 four-letter code used to represent the Tai Tham script in international standards.
-
C.
Lana
Lana is a professional wrestler and television personality best known for her time in WWE, where she appeared prominently as a manager and in-ring performer.
-
D.
Lana
Lana is the given name of actress Lana Condor, best known for starring in the "To All the Boys I've Loved Before" film series.
-
E.
Lana
chosen
Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24598c9488190a92df7d8b1824724 |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000ee788f88190b16d267f1eee6d62 |
completed | May 10, 2026, 4:51 a.m. |
Created at: April 10, 2026, 5:04 a.m.