Triple
T19595488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valmiki’s hermitage |
E470337
|
entity |
| Predicate | providesRefugeTo |
P55236
|
FINISHED |
| Object | Lava |
—
|
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: Lava | Statement: [Valmiki’s hermitage, providesRefugeTo, Lava]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lava Context triple: [Valmiki’s hermitage, providesRefugeTo, Lava]
-
A.
Lava
chosen
Lava is a legendary prince in the Hindu epic Ramayana, known as one of the twin sons of Rama and Sita.
-
B.
Lava
Lava is a small hill town in West Bengal, India, known as a gateway to the Neora Valley National Park and for its cool climate and forested surroundings.
-
C.
Lava
Lava is a surname most notably borne by American film and television composer William Lava, known for his work on numerous Warner Bros. cartoons and Westerns.
-
D.
Lava
"Lava" is a nonfiction book by Andrea Warren that explores the science, danger, and human stories surrounding volcanic eruptions.
-
E.
Lava
Lava is a Pixar animated musical short film that tells a romantic, volcano-themed love story through a Hawaiian-inspired song.
- 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e6407a09608190aa1df92f8c21f9e7 |
completed | April 20, 2026, 3:04 p.m. |
Created at: April 10, 2026, 1:43 p.m.