Triple
T16247633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bus Stop (play) |
E394414
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Cherie |
E394416
|
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: Cherie | Statement: [Bus Stop (play), hasMainCharacter, Cherie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cherie Context triple: [Bus Stop (play), hasMainCharacter, Cherie]
-
A.
Cherie
chosen
Cherie is the naive yet determined young woman who becomes the romantic focus of the cowboy in the classic stage play and film "Bus Stop."
-
B.
Charlene
Charlene is a feminine given name derived from the male name Charles.
-
C.
Cherrelle
Cherrelle is an American R&B singer best known for her 1980s hits and collaborations with producers Jimmy Jam and Terry Lewis.
-
D.
Valerie Cherish
Valerie Cherish is a fading sitcom actress desperately seeking renewed fame and validation in the reality-TV-obsessed Hollywood landscape.
-
E.
Charmaine
Charmaine is a flirtatious French innkeeper’s daughter who becomes the central romantic interest and source of rivalry between two soldiers in the World War I play and film "What Price Glory."
- 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_69e245942460819080897afad0d2fe09 |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000ee3bbc48190a56ce2807a9510f0 |
completed | May 10, 2026, 4:51 a.m. |
Created at: April 10, 2026, 5:04 a.m.