Triple
T16637745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1 Corinthians 11 |
E404251
|
entity |
| Predicate | mentionsPerson |
P831
|
FINISHED |
| Object |
Paul
Paul is an early Christian apostle and missionary whose letters form a significant portion of the New Testament and profoundly shaped Christian theology.
|
E1224496
|
NE FINISHED |
How this triple was built (4 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: [1 Corinthians 11, mentionsPerson, Paul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Context triple: [1 Corinthians 11, mentionsPerson, 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
Paul is a 2011 sci-fi comedy film about two British geeks who encounter a wisecracking alien during a road trip across the United States.
-
D.
Paul
Paul is a fictional character from Wallace Thurman's Harlem Renaissance novel "Infants of the Spring," which satirically portrays life in a bohemian Black artists' colony.
-
E.
Paul
Paul is a character in the film "Her," known as Theodore Twombly’s supportive and easygoing close friend.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Paul Triple: [1 Corinthians 11, mentionsPerson, Paul]
Generated description
Paul is an early Christian apostle and missionary whose letters form a significant portion of the New Testament and profoundly shaped Christian theology.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paul Target entity description: Paul is an early Christian apostle and missionary whose letters form a significant portion of the New Testament and profoundly shaped Christian theology.
-
A.
Paul
Paul is a masculine given name of Latin origin, widely used in many Western and Christian-influenced cultures.
-
B.
Paul
Paul is a character in the film "Her," known as Theodore Twombly’s supportive and easygoing close friend.
-
C.
Paul
Paul is the central character of the story "Paul the Peddler," depicted as a resourceful young street vendor navigating the challenges of urban life.
-
D.
Paul
Paul is a central character in the psychological horror film "It Comes at Night," portrayed as a protective family man struggling to safeguard his loved ones amid a mysterious, apocalyptic threat.
-
E.
Paul
Paul is a village and civil parish in Cornwall, England, known for its historic church and coastal setting near Penzance.
- F. None of above. chosen
Provenance (5 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e378eb07388190a10e896bae7f098e |
completed | April 18, 2026, 12:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007dc28df48190b01c1328df24df60 |
completed | May 10, 2026, 12:44 p.m. |
| NEDg | Description generation | batch_6a007e8cff9881908c6b86da38fc2f08 |
completed | May 10, 2026, 12:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a007f53918481908d84ecf50a562266 |
completed | May 10, 2026, 12:51 p.m. |
Created at: April 10, 2026, 5:18 a.m.