Triple
T19415557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panchkula district |
E485704
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Kalka |
—
|
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: Kalka | Statement: [Panchkula district, contains, Kalka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalka Context triple: [Panchkula district, contains, Kalka]
-
A.
Kalka
chosen
Kalka is a town in the Indian state of Haryana, known as a gateway to the Himalayan hill stations and the starting point of the Kalka–Shimla railway.
-
B.
Lakdikapul
Lakdikapul is a prominent commercial and transit locality in central Hyderabad, known for its busy roads, hotels, and connectivity to major city hubs.
-
C.
Kundarki
Kundarki is a notable town in the Moradabad district of Uttar Pradesh, India, serving as a local hub for trade and administration.
-
D.
Chamkoria
Chamkoria is the former name of Borovets, one of Bulgaria’s oldest and most popular mountain ski resorts.
-
E.
Kanchrapara
Kanchrapara is a town in the North 24 Parganas district of West Bengal, India, known historically for its railway workshop and suburban connectivity to Kolkata.
- 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e62af90758819088999d98d270011a |
completed | April 20, 2026, 1:32 p.m. |
Created at: April 10, 2026, 1:37 p.m.