Triple
T5884821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Life of Pi |
E130835
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Santosh Patel |
E549423
|
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: Santosh Patel | Statement: [Life of Pi, featuresCharacter, Santosh Patel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santosh Patel Context triple: [Life of Pi, featuresCharacter, Santosh Patel]
-
A.
Santosh Patel
chosen
Santosh Patel is the practical, zoo-owning father of protagonist Piscine Molitor Patel in Yann Martel’s novel "Life of Pi."
-
B.
Naren Patel
Naren Patel is a notable individual distinguished by achievements significant enough to be specifically recognized among people with the surname Patel.
-
C.
Ravindra Patel
Ravindra Patel is a notable individual bearing the surname Patel, recognized for achievements significant enough to be distinctly recorded.
-
D.
Kumar Patel
Kumar Patel is a laid-back, marijuana-loving Korean American character from the "Harold & Kumar" comedy film series, known for his misadventurous escapades with his best friend Harold Lee.
-
E.
Sanjay Patel
Sanjay Patel is a common Indian name shared by several notable individuals, including professionals in fields such as animation, business, and academia.
- 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_69c0085628dc8190b334c1b44c067efc |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0367743508190bae211e9ce8f9690 |
completed | March 22, 2026, 6:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1134bb82881908b912f96a3b6f0f1 |
completed | March 23, 2026, 10:17 a.m. |
Created at: March 22, 2026, 3:57 p.m.