Triple
T5852335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Evan Schiff |
E130064
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Peppermint
Peppermint is a 2018 American action thriller film starring Jennifer Garner as a mother seeking vigilante justice after the murder of her family.
|
E189820
|
NE FINISHED |
How this triple was built (4 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: Peppermint | Statement: [Evan Schiff, notableWork, Peppermint]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peppermint Context triple: [Evan Schiff, notableWork, Peppermint]
-
A.
Peppermint
Peppermint is a 2018 American action thriller film starring Jennifer Garner as a vigilante seeking revenge for her family's murder.
-
B.
Doublemint
Doublemint is a popular Wrigley chewing gum brand known for its long-lasting mint flavor and iconic twin-themed advertising.
-
C.
Cinnamon
Cinnamon is a modern, user-friendly desktop environment for Linux systems that offers a traditional, Windows-like interface with advanced customization options.
-
D.
Mentha
Mentha is a genus of aromatic herbs commonly known as mints, widely used for culinary, medicinal, and aromatic purposes.
-
E.
Pfeffer
Pfeffer is a German-origin surname borne by various notable individuals across fields such as academia, politics, and the arts.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Peppermint Triple: [Evan Schiff, notableWork, Peppermint]
Generated description
Peppermint is a 2018 American action thriller film starring Jennifer Garner as a mother seeking vigilante justice after the murder of her family.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peppermint Target entity description: Peppermint is a 2018 American action thriller film starring Jennifer Garner as a mother seeking vigilante justice after the murder of her family.
-
A.
Peppermint
chosen
Peppermint is a 2018 American action thriller film starring Jennifer Garner as a vigilante seeking revenge for her family's murder.
-
B.
Doublemint
Doublemint is a popular Wrigley chewing gum brand known for its long-lasting mint flavor and iconic twin-themed advertising.
-
C.
Cinnamon
Cinnamon is a modern, user-friendly desktop environment for Linux systems that offers a traditional, Windows-like interface with advanced customization options.
-
D.
Mentha
Mentha is a genus of aromatic herbs commonly known as mints, widely used for culinary, medicinal, and aromatic purposes.
-
E.
Pfeffer
Pfeffer is a German-origin surname borne by various notable individuals across fields such as academia, politics, and the arts.
- F. None of above.
Provenance (5 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0355038008190bf38980349b533e2 |
completed | March 22, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a1b8f8508190942ce1725884d254 |
completed | March 23, 2026, 2:13 a.m. |
| NEDg | Description generation | batch_69c0a30d66088190b6e37a95527b273d |
completed | March 23, 2026, 2:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0a364931c81908d595d74cb4f9a7b |
completed | March 23, 2026, 2:20 a.m. |
Created at: March 22, 2026, 3:55 p.m.