Triple
T19597617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malayattoor |
E470387
|
entity |
| Predicate | distanceFromAngamaly |
P59300
|
FINISHED |
| Object | approximately 15 km from Angamaly |
—
|
LITERAL 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: approximately 15 km from Angamaly | Statement: [Malayattoor, distanceFromAngamaly, approximately 15 km from Angamaly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromAngamaly Context triple: [Malayattoor, distanceFromAngamaly, approximately 15 km from Angamaly]
-
A.
distanceFromAngamaly_km
chosen
Indicates the distance, measured in kilometers, between a given place and Angamaly.
-
B.
distanceToKanyakumari
Indicates the spatial distance between a given location and Kanyakumari.
-
C.
distanceFromBengaluru
Indicates the measured spatial distance between a given entity’s location and the city of Bengaluru.
-
D.
distanceFromTirupati
Indicates the measured distance between a given location and Tirupati.
-
E.
distanceFromMunnar
Indicates the spatial distance measured from the reference location Munnar to another place or entity.
- F. None of above.
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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e6407c52c081908704d3a4dd6e853b |
completed | April 20, 2026, 3:04 p.m. |
| PD | Predicate disambiguation | batch_69e514e166dc8190a0f147e0b4c8bbe7 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:43 p.m.