Triple
T33884978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The President Vanishes |
E868602
|
entity |
| Predicate | featuresFictionalCharacterRole |
P23263
|
FINISHED |
| Object | President of the United States |
E112
|
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: President of the United States | Statement: [The President Vanishes, featuresFictionalCharacterRole, President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresFictionalCharacterRole Context triple: [The President Vanishes, featuresFictionalCharacterRole, President of the United States]
-
A.
featuresCharacterRole
chosen
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
B.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
-
C.
filmCharacterDescribedAs
Indicates that a film character is described or characterized using a particular attribute, phrase, or depiction.
-
D.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
-
E.
featuresReturningCharacterFrom
Indicates that a work includes the reappearance of a character who previously appeared in the referenced source work.
- 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_69f34995b81c8190acdb45cea5a10eff |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a0235be932081908857bc3a77c27e7d |
completed | May 11, 2026, 8:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3676321d0081908d6e4275aa79e457 |
completed | June 20, 2026, 11:14 a.m. |
| PD | Predicate disambiguation | batch_6a022c865dec81909e02b2f330e987e6 |
completed | May 11, 2026, 7:22 p.m. |
Created at: May 1, 2026, 1:48 a.m.