Triple
T14528167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bolt |
E340832
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Penny |
E193351
|
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: Penny | Statement: [Bolt, mainCharacter, Penny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Penny Context triple: [Bolt, mainCharacter, Penny]
-
A.
Penny
Penny Pritzker is an American billionaire businesswoman, civic leader, and former U.S. Secretary of Commerce in the Obama administration.
-
B.
Penny
Penny is a familiar diminutive form of the given name Penelope, often used as a friendly and informal nickname.
-
C.
Penny
Penny is a central character in the educational context of "Teachers," likely portrayed as a key figure around whom classroom stories and interactions revolve.
-
D.
Penny
Penny is a central character in Ali Smith's novel "Hotel World," around whom much of the story's emotional and thematic exploration of life, death, and connection revolves.
-
E.
Penny
chosen
Penny is a friendly, aspiring actress and waitress who becomes the sociable, down-to-earth neighbor and later close friend and love interest of the main nerdy characters in the sitcom "The Big Bang Theory."
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea051bc608190ad4d516c5e7bca43 |
completed | April 14, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a547c408190a1a19e12aac1d5bd |
completed | May 8, 2026, 5:53 a.m. |
Created at: April 10, 2026, 1:22 a.m.