Triple
T20208699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. No |
E493427
|
entity |
| Predicate | hasFictionalSecretBase |
P61563
|
FINISHED |
| Object | Dr. Julius No's island headquarters |
—
|
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: Dr. Julius No's island headquarters | Statement: [Dr. No, hasFictionalSecretBase, Dr. Julius No's island headquarters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalSecretBase Context triple: [Dr. No, hasFictionalSecretBase, Dr. Julius No's island headquarters]
-
A.
hasSecretFacility
chosen
Indicates that an entity possesses or controls a hidden or undisclosed facility.
-
B.
hasFictionalEstablishmentType
Indicates that an establishment is associated with a particular type or category of fictional setting or institution.
-
C.
hasFictionalLocation
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
D.
hasFictionalLandmark
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
-
E.
hasVillainBaseLocation
Indicates that a villain’s primary base or headquarters is located at a specified place.
- 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66d94999c8190b67051cb0acea213 |
completed | April 20, 2026, 6:16 p.m. |
| PD | Predicate disambiguation | batch_69e55b14c9d8819095453d0504d9222f |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:38 p.m.