Triple
T21202799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raseedi Ticket |
E522498
|
entity |
| Predicate | relatedWork |
P37
|
FINISHED |
| Object | Pinjar |
—
|
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: Pinjar | Statement: [Raseedi Ticket, relatedWork, Pinjar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pinjar Context triple: [Raseedi Ticket, relatedWork, Pinjar]
-
A.
Pinjar
chosen
Pinjar is a landmark Punjabi novel that poignantly portrays the human cost of the Partition of India, especially through the suffering and resilience of women.
-
B.
Jhirnya
Jhirnya is a small town in the Khargone district of Madhya Pradesh, India, known primarily as a local administrative and rural market center.
-
C.
Pinara
Pinara was an important ancient Lycian city in southwestern Anatolia, known for its rock-cut tombs and well-preserved ruins.
-
D.
Pinlebu
Pinlebu is a small town in northern Myanmar known for serving as a local hub for surrounding rural communities.
-
E.
Pinnau
The Pinnau is a river in northern Germany that flows through the district of Pinneberg in the state of Schleswig-Holstein before emptying into the Elbe.
- 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_69e0b5112d8881909510b2dcdc93106d |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73432a2b88190a89e636626d40c9b |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 3:19 p.m.