Triple
T25906574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lymelife |
E652766
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Melissa Bragg
Melissa Bragg is a fictional character in the coming-of-age drama film "Lymelife," which explores family turmoil and suburban life in 1970s Long Island.
|
E1785769
|
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: Melissa Bragg | Statement: [Lymelife, hasCharacter, Melissa Bragg]
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: Melissa Bragg Triple: [Lymelife, hasCharacter, Melissa Bragg]
Generated description
Melissa Bragg is a fictional character in the coming-of-age drama film "Lymelife," which explores family turmoil and suburban life in 1970s Long Island.
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_69e7ab3d3f8481909bc53ed64c06af33 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f603c0298881908717be820df8ab0f |
completed | May 2, 2026, 2:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12e429880481909b189e689009be76 |
completed | May 24, 2026, 11:42 a.m. |
| NEDg | Description generation | batch_6a12e4fbf9cc8190b5bbff117668f81a |
completed | May 24, 2026, 11:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12e5cfee048190a139532d8e125411 |
completed | May 24, 2026, 11:49 a.m. |
Created at: April 22, 2026, 8:27 a.m.