Triple
T16253561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prayers for Bobby |
E394573
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Katie Ford |
E1332873
|
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: Katie Ford | Statement: [Prayers for Bobby, screenwriter, Katie Ford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katie Ford Context triple: [Prayers for Bobby, screenwriter, Katie Ford]
-
A.
Katie Ford
chosen
Katie Ford is a Canadian-American screenwriter best known for co-writing the hit comedy film "Miss Congeniality" and for her work in television.
-
B.
Katie Featherston
Katie Featherston is an American actress best known for her role as Katie in the "Paranormal Activity" horror film series.
-
C.
Katie Lyons
Katie Lyons is a British actress known for her comedic roles in television series such as Green Wing and other UK comedies.
-
D.
Katie Cox
Katie Cox is a supporting character in the dark comedy film "Burn After Reading," known as the unfaithful wife of a CIA analyst whose affair helps set off the movie’s chain of chaotic events.
-
E.
Katie Boyle
Katie Boyle was an Italian-born British television presenter and actress best known for hosting the Eurovision Song Contest multiple times in the 1960s and 1970s.
- 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_6a0705b8a3f481909c748d6ff6f27f64 |
completed | May 15, 2026, 11:38 a.m. |
Created at: April 10, 2026, 5:04 a.m.