Triple
T24771624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. James Warwick |
E619739
|
entity |
| Predicate | hasDaughter |
P24357
|
FINISHED |
| Object |
Mary Warwick
Mary Warwick is the daughter of fictional physician Dr. James Warwick in the "Doctor Who" universe.
|
E1662700
|
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: Mary Warwick | Statement: [Dr. James Warwick, hasDaughter, Mary Warwick]
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: Mary Warwick Triple: [Dr. James Warwick, hasDaughter, Mary Warwick]
Generated description
Mary Warwick is the daughter of fictional physician Dr. James Warwick in the "Doctor Who" universe.
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_69e2fabd04488190a2d13c97be745a2d |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f410abf6588190ac997f02a1177c19 |
completed | May 1, 2026, 2:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a104882d9e88190afc2212fb54d0b73 |
completed | May 22, 2026, 12:13 p.m. |
| NEDg | Description generation | batch_6a10496ad0748190b797fea89fc9472d |
completed | May 22, 2026, 12:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a104bbb9b6c81908fcc21c8c027b9de |
completed | May 22, 2026, 12:27 p.m. |
Created at: April 18, 2026, 4:31 a.m.