Triple
T2658396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | What's Eating Gilbert Grape |
E54667
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Becky |
E38129
|
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: Becky | Statement: [What's Eating Gilbert Grape, mainCharacter, Becky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Becky Context triple: [What's Eating Gilbert Grape, mainCharacter, Becky]
-
A.
Becky
chosen
Becky is a common English feminine given name, typically used as a diminutive of Rebecca.
-
B.
Bella Higginbotham
Bella Higginbotham is an American actress best known for her role in the film "Troop Zero" and for appearing in various television and streaming series.
-
C.
Rebeca
Rebeca is a feminine given name, commonly used in Spanish- and Portuguese-speaking countries, that is a variant of the name Rebecca.
-
D.
Lauren
Lauren is a central female protagonist in the romantic comedy film "Think Like a Man," portrayed as a successful, relationship-seeking woman whose love life is influenced by Steve Harvey’s dating advice.
-
E.
Julie Beckman
Julie Beckman is an American architect best known for co-designing the National 9/11 Pentagon Memorial in Arlington, Virginia.
- 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_69ab49e028948190b97e01d73548b1d9 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd94dcaa48190aec625f68ce61a02 |
completed | March 7, 2026, 7:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa054d4d0819095084088fd54a63a |
completed | March 10, 2026, 4:38 a.m. |
Created at: March 6, 2026, 9:53 p.m.