Triple
T4783477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalady |
E106419
|
entity |
| Predicate | nearbyLocality |
P4647
|
FINISHED |
| Object | Mattoor |
E470378
|
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: Mattoor | Statement: [Kalady, nearbyLocality, Mattoor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mattoor Context triple: [Kalady, nearbyLocality, Mattoor]
-
A.
Angamaly
chosen
Angamaly is a town in the Ernakulam district of Kerala, India, known as a major transportation hub and gateway to the nearby Cochin International Airport.
-
B.
Panambi
Panambi is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
-
C.
Alappuzha
Alappuzha is a coastal city in the Indian state of Kerala, famed for its backwaters, houseboat cruises, and annual Nehru Trophy boat race.
-
D.
Kayadhu
Kayadhu is a figure in Hindu mythology known as the wife of the demon king Hiranyakashipu and the mother of the devotee Prahlada.
-
E.
Mavalli
Mavalli is a neighborhood in Bengaluru, India, known for its proximity to Lalbagh Botanical Garden and its mix of residential areas, local markets, and historic eateries.
- 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_69bd43f4a9588190bf73e20bc27c03cc |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd65ad3a188190872e47e3a3bf504b |
completed | March 20, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be4d93f2cc8190acf96766d2c5a946 |
completed | March 21, 2026, 7:49 a.m. |
Created at: March 20, 2026, 1:22 p.m.