Triple
T35142391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Troop Beverly Hills |
E1014727
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Pamela Norris
Pamela Norris is an American television and film writer best known for co-writing the 1989 comedy film "Troop Beverly Hills" and her work on popular TV sitcoms.
|
E2223702
|
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: Pamela Norris | Statement: [Troop Beverly Hills, screenwriter, Pamela Norris]
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: Pamela Norris Triple: [Troop Beverly Hills, screenwriter, Pamela Norris]
Generated description
Pamela Norris is an American television and film writer best known for co-writing the 1989 comedy film "Troop Beverly Hills" and her work on popular TV sitcoms.
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_69f76dda7c108190a2ffd93eb6c341a7 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78cab90cc8190827145ac515d203b |
completed | May 3, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a406cb80ab48190b9421e53f275454a |
completed | June 28, 2026, 12:37 a.m. |
| NEDg | Description generation | batch_6a406defac38819086c2585b63b93dde |
completed | June 28, 2026, 12:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a406e7763b081908b37db6d670f2084 |
completed | June 28, 2026, 12:44 a.m. |
Created at: May 3, 2026, 4:02 p.m.