Triple
T38213565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Wolf |
E1010620
|
entity |
| Predicate | associatedWithIncarnationOfTheDoctor |
P113557
|
FINISHED |
| Object | Ninth Doctor |
E360379
|
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: Ninth Doctor | Statement: [Bad Wolf, associatedWithIncarnationOfTheDoctor, Ninth Doctor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithIncarnationOfTheDoctor Context triple: [Bad Wolf, associatedWithIncarnationOfTheDoctor, Ninth Doctor]
-
A.
featuresIncarnationOfDoctor
Indicates that the subject includes or presents a specific incarnation (version) of the Doctor character.
-
B.
associatedDoctorIncarnation
chosen
Indicates a relationship where a particular doctor is linked to a specific incarnation or version of that doctor.
-
C.
relationToTimeLords
Indicates the nature of a subject’s connection, association, or relevance to Time Lords, such as affiliation, interaction, or status in relation to them.
-
D.
laterBecameCompanionOf
Indicates that, after an earlier point in time, one entity subsequently became the companion of another entity.
-
E.
firstAppearanceAsTheDoctor
Indicates the event or work in which an actor portrays the Doctor character for the first time.
- F. None of above.
Provenance (4 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_69f76dcdc7708190a5f1751d53f40ffe |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a419decf6bc8190b29cfa269785af54 |
completed | June 28, 2026, 10:19 p.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:30 p.m.