Triple
T5008317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean-Paul |
E112550
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Paul |
E3700
|
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: Paul | Statement: [Jean-Paul, hasComponent, Paul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Context triple: [Jean-Paul, hasComponent, Paul]
-
A.
Paul
Paul is the middle-aged American widower portrayed by Marlon Brando in the controversial 1972 film "Last Tango in Paris."
-
B.
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."
-
C.
Paul
chosen
Paul is a masculine given name of Latin origin, widely used in many Western and Christian-influenced cultures.
-
D.
Paulus
Paulus was an influential Roman jurist whose legal writings significantly shaped later compilations of Roman law.
-
E.
Apostle Paul
Apostle Paul was an early Christian missionary and theologian whose letters form a significant portion of the New Testament and profoundly shaped Christian doctrine.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd4433d0b08190877e83959ef40d81 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd72eb05f881908d7dc3d7cd07b2ae |
completed | March 20, 2026, 4:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9266314881909dfb710c5b5c8f65 |
completed | March 21, 2026, 12:43 p.m. |
Created at: March 20, 2026, 1:35 p.m.