Triple
T20237558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suchitra Sen |
E498190
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Harano Sur |
—
|
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: Harano Sur | Statement: [Suchitra Sen, notableWork, Harano Sur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harano Sur Context triple: [Suchitra Sen, notableWork, Harano Sur]
-
A.
Harano Sur
chosen
Harano Sur is a classic Bengali romantic drama film starring Suchitra Sen, celebrated for its poignant love story and memorable music.
-
B.
Uenohara
Uenohara is a city in Yamanashi Prefecture, Japan, known for its mountainous terrain and role as a regional transport and residential hub near the Tokyo metropolitan area.
-
C.
Sadaharu
Sadaharu is the given name of Sadaharu Oh, the legendary Japanese-Taiwanese baseball player and home run record holder.
-
D.
Hanazono
Hanazono is a popular ski and outdoor recreation area within the Niseko resort region of Hokkaido, Japan, known for its powder snow and winter sports facilities.
-
E.
Hanazono
Hanazono is a historic rugby stadium in Higashiosaka, Japan, renowned as a major venue for high school and professional rugby matches.
- 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6716b4c148190bf663b8a747fbfa5 |
completed | April 20, 2026, 6:33 p.m. |
Created at: April 11, 2026, 11:40 p.m.