Triple
T30988299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Daring Young Man |
E789592
|
entity |
| Predicate | hasNotableScreenRoleOf |
P169911
|
FINISHED |
| Object | Lupe Vélez |
—
|
NE NERFINISHED |
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: Lupe Vélez | Statement: [The Daring Young Man, hasNotableScreenRoleOf, Lupe Vélez]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableScreenRoleOf Context triple: [The Daring Young Man, hasNotableScreenRoleOf, Lupe Vélez]
-
A.
hasNotableRoleIn
Indicates that an entity holds a significant or noteworthy role or function within another entity, event, work, or context.
-
B.
hasMainRole
Indicates that an entity holds the primary or most significant role in relation to another entity or context.
-
C.
hasHumanCharacterRole
Indicates that an entity is assigned a role or function specifically associated with a human character within a context such as a story, performance, or representation.
-
D.
hasNotablePortrayerOccupation
Indicates that the occupation specified is a notable profession of a person who portrays the given entity (such as an actor playing a character).
-
E.
hasPlayingRole
chosen
Indicates that an entity participates in an activity, event, or performance in a specific playing role or capacity.
- 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_69f224c550b081909ddfceb0c3d03bdd |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a007338002081908cb6340d65ff86d5 |
completed | May 10, 2026, 11:59 a.m. |
| PD | Predicate disambiguation | batch_6a0072a137ac8190a7debeb28e738e03 |
completed | May 10, 2026, 11:57 a.m. |
Created at: April 29, 2026, 8:56 p.m.