Triple
T20141109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Things Heard & Seen |
E491165
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Ana Sophia Heger |
—
|
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: Ana Sophia Heger | Statement: [Things Heard & Seen, starring, Ana Sophia Heger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ana Sophia Heger Context triple: [Things Heard & Seen, starring, Ana Sophia Heger]
-
A.
Ana Sophia Heger
chosen
Ana Sophia Heger is an actress known for her role on the American television sitcom "Life in Pieces."
-
B.
Alexandra Henkel
Alexandra Henkel is known as the wife of British actor Adeel Akhtar.
-
C.
Alexandra Scherer
Alexandra Scherer is a German local politician who serves as the mayor of the spa town Bad Wurzach in Baden-Württemberg.
-
D.
Julia Sauer
Julia Sauer was an American librarian and author best known for her atmospheric children's fantasy and historical novels, including the Newbery Honor book "Fog Magic."
-
E.
Hannah von Reichmerl
Hannah von Reichmerl is a mysterious, ethereal young woman central to the unsettling secrets of the Alpine wellness center in the psychological horror film "A Cure for Wellness."
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6679b179c8190a9511df8ed82098a |
completed | April 20, 2026, 5:51 p.m. |
Created at: April 11, 2026, 11:32 p.m.