Triple
T16794163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mumbai–Pune railway line |
E408188
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Karjat |
E704185
|
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: Karjat | Statement: [Mumbai–Pune railway line, passesThrough, Karjat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karjat Context triple: [Mumbai–Pune railway line, passesThrough, Karjat]
-
A.
Karjat
chosen
Karjat is a town in Maharashtra, India, known as a scenic weekend getaway and trekking hub in the Western Ghats, popular for its waterfalls, forts, and proximity to Mumbai and Pune.
-
B.
Karhula
Karhula is a former Finnish municipality and industrial community that was incorporated into the city of Kotka.
-
C.
Karinska
Karinska was a renowned 20th-century costume designer best known for her influential work in ballet and theater, particularly with the New York City Ballet.
-
D.
Kataja
Kataja is a short form or nickname of the female given name Katarina.
-
E.
Juankoski
Juankoski is a small town and former industrial municipality in eastern Finland, known for its historical ironworks and lakeside scenery.
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2a8b66c8190b83d8af85d2b23a8 |
completed | April 18, 2026, 4:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00ab0e1e9c8190bb2ef0825b25f6e5 |
completed | May 10, 2026, 3:58 p.m. |
Created at: April 10, 2026, 5:22 a.m.