Triple
T9211271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helen Deutsch |
E221122
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Helen Deutsch |
E221122
|
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: Helen Deutsch | Statement: [Helen Deutsch, name, Helen Deutsch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Helen Deutsch Context triple: [Helen Deutsch, name, Helen Deutsch]
-
A.
Helen Deutsch
chosen
Helen Deutsch was an American screenwriter best known for her work on classic mid-20th-century Hollywood films.
-
B.
Helene Deutsch
Helene Deutsch was a pioneering psychoanalyst best known for her influential work on female psychology and motherhood within the early Freudian movement.
-
C.
Alice Arlen
Alice Arlen was an American screenwriter best known for co-writing acclaimed films such as "Silkwood" and "Alamo Bay."
-
D.
Helen Soby
Helen Soby is best known as the former wife of British television presenter and media personality Noel Edmonds.
-
E.
Helen Mosel
Helen Mosel, better known as Helen Wolff, was a notable German-American editor and publisher recognized for bringing important European literature to English-speaking audiences.
- 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_69ca83e9d0e081908bdb71097201a06c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd9b69838819088f33ca995fce222 |
completed | April 1, 2026, 8:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d077875eac8190a020dfa38998385e |
completed | April 4, 2026, 2:29 a.m. |
Created at: March 30, 2026, 7:27 p.m.