Triple
T6828968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Post |
E157087
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Liz Hannah |
E273182
|
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: Liz Hannah | Statement: [The Post, screenwriter, Liz Hannah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liz Hannah Context triple: [The Post, screenwriter, Liz Hannah]
-
A.
Liz Hannah
chosen
Liz Hannah is an American screenwriter and producer best known for co-writing the acclaimed historical drama film "The Post."
-
B.
Lisa Hallett
Lisa Hallett is a character in the British science fiction series "Torchwood," known for her tragic storyline involving cybernetic conversion and her relationship with Ianto Jones.
-
C.
Alice Hanthorn
Alice Hanthorn was the wife of American businessman and government official Lewis L. Strauss, associated with his early life and career before his prominence in nuclear policy.
-
D.
Liza Snyder
Liza Snyder is an American television actress best known for her comedic roles on sitcoms such as "Yes, Dear" and "Man with a Plan."
-
E.
Liz Watts
Liz Watts is an Australian film and television producer known for her work on acclaimed projects such as the crime drama film "Animal Kingdom."
- 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_69c6882a5b5c8190917a7db9ed36bad1 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d6254bd88190a2a424537c2c12e2 |
completed | March 27, 2026, 7:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7881e300881909c1cad76bc8b23ff |
completed | March 28, 2026, 7:49 a.m. |
Created at: March 27, 2026, 2:18 p.m.