Triple
T19470820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John-Paul |
E487117
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Paul |
—
|
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: Paul | Statement: [John-Paul, hasComponent, Paul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Context triple: [John-Paul, hasComponent, Paul]
-
A.
Paul
Paul is a laid-back, charming sperm donor whose unexpected involvement with his biological children disrupts a lesbian couple’s family dynamic in the film "The Kids Are All Right."
-
B.
Paul
Paul is a character from the film "Nobody’s Business," contributing to the story’s exploration of personal and family relationships.
-
C.
Paul
Paul is the tormented protagonist of Erich Wolfgang Korngold’s opera "Die tote Stadt," struggling with grief and obsession over his dead wife in the decaying city of Bruges.
-
D.
Paul
Paul is a character in the film "Her," known as Theodore Twombly’s supportive and easygoing close friend.
-
E.
Paul
chosen
Paul is a masculine given name of Latin origin, widely used in many Western and Christian-influenced cultures.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633e809008190b0021d41b99f9700 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:39 p.m.