Triple
T36540470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The TV Movie |
E900705
|
entity |
| Predicate | portraysDoctorNumber |
P50208
|
FINISHED |
| Object | 8 |
—
|
LITERAL 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: 8 | Statement: [The TV Movie, portraysDoctorNumber, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysDoctorNumber Context triple: [The TV Movie, portraysDoctorNumber, 8]
-
A.
portrayedDoctorBy
Indicates that one entity served in the role of portraying a doctor character associated with another entity (such as a show, film, or franchise).
-
B.
doctorNumber
chosen
Indicates the unique identifying number assigned to a doctor in the context of a relationship or record.
-
C.
hasSeriesDoctor
Indicates that a particular doctor is associated with or responsible for a given series (such as a TV show, book series, or medical series).
-
D.
hasDoctorCharacter
Indicates that an entity includes or features a character whose role or profession is that of a doctor.
-
E.
featuredDoctor
Indicates that a particular doctor is highlighted or promoted as a primary or notable medical professional in a given context.
- F. None of above.
Provenance (3 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_69f76e5fbb388190b70c4c15573c8143 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ff795d25d08190b7584c72be39d309 |
completed | May 9, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69ff78a90fbc8190a62c57456dc1d4ad |
completed | May 9, 2026, 6:10 p.m. |
Created at: May 3, 2026, 4:11 p.m.