Triple
T25156897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rent: Filmed Live on Broadway |
E626336
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Rodney Hicks
Rodney Hicks is an American stage actor and singer best known for his performances in Broadway productions such as Rent and Come From Away.
|
E1670925
|
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: Rodney Hicks | Statement: [Rent: Filmed Live on Broadway, hasCastMember, Rodney Hicks]
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: Rodney Hicks Triple: [Rent: Filmed Live on Broadway, hasCastMember, Rodney Hicks]
Generated description
Rodney Hicks is an American stage actor and singer best known for his performances in Broadway productions such as Rent and Come From Away.
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_69e2ff2834ec8190b0872e2ec3d76023 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f46b8852c88190b40ecc443c3f0957 |
completed | May 1, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1067c45a2c8190a1d265a67f5e574a |
completed | May 22, 2026, 2:27 p.m. |
| NEDg | Description generation | batch_6a1068d5ff248190b9efb77366147c26 |
completed | May 22, 2026, 2:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1069d0ba8c81908b38818567784552 |
completed | May 22, 2026, 2:36 p.m. |
Created at: April 18, 2026, 6:30 a.m.