Triple
T14487701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hothouse |
E359278
|
entity |
| Predicate | hasMainCharacterProfession |
P21567
|
FINISHED |
| Object | psychiatrist |
—
|
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: psychiatrist | Statement: [Hothouse, hasMainCharacterProfession, psychiatrist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainCharacterProfession Context triple: [Hothouse, hasMainCharacterProfession, psychiatrist]
-
A.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
B.
hasProfessionTrait
Indicates that an entity possesses a particular characteristic, quality, or attribute specifically related to their profession or occupational role.
-
C.
hasMainRole
Indicates that an entity holds the primary or most significant role in relation to another entity or context.
-
D.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
-
E.
hasNotableProfessionDistributionIn
Indicates that the distribution or prevalence of notable professions associated with an entity is observed or characterized within a specified context, such as a location or group.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de924ee0f08190baf68318b41fa64d |
completed | April 14, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69de5c487b4c819097803e58dca628a5 |
completed | April 14, 2026, 3:24 p.m. |
Created at: April 10, 2026, 1:20 a.m.