Triple
T29778317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Young Doctors |
E755441
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object |
Nurse Lisa Brooks
Nurse Lisa Brooks is a central character in the Australian medical drama series "The Young Doctors," portrayed as a dedicated and compassionate hospital nurse.
|
E1888276
|
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: Nurse Lisa Brooks | Statement: [The Young Doctors, hasMainCharacter, Nurse Lisa Brooks]
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: Nurse Lisa Brooks Triple: [The Young Doctors, hasMainCharacter, Nurse Lisa Brooks]
Generated description
Nurse Lisa Brooks is a central character in the Australian medical drama series "The Young Doctors," portrayed as a dedicated and compassionate hospital nurse.
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_69f0ef878574819088c867fd1a5c8b86 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69f674a363848190814f687a63333026 |
completed | May 2, 2026, 10:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a26f1b3f10881908ddc47b72a40fb4c |
completed | June 8, 2026, 4:45 p.m. |
| NEDg | Description generation | batch_6a26f28951d0819093b834f08eff940b |
completed | June 8, 2026, 4:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a26f365e1448190bc8539feec582fd7 |
completed | June 8, 2026, 4:52 p.m. |
Created at: April 28, 2026, 8:48 p.m.