Triple
T15296872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gil Pender |
E365682
|
entity |
| Predicate | conflictWith |
P4897
|
FINISHED |
| Object | Paul |
E1112391
|
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: [Gil Pender, conflictWith, Paul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Context triple: [Gil Pender, conflictWith, 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
chosen
Paul is a character in the film "Her," known as Theodore Twombly’s supportive and easygoing close friend.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e036848c1881908fbaaae0216d6d27 |
completed | April 16, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef82f6d08190b809260dda247dfe |
completed | May 9, 2026, 8:25 a.m. |
Created at: April 10, 2026, 3:15 a.m.