Triple
T19456978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wandoor Jetty |
E486759
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Wandoor |
—
|
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: Wandoor | Statement: [Wandoor Jetty, locatedIn, Wandoor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wandoor Context triple: [Wandoor Jetty, locatedIn, Wandoor]
-
A.
Wandoor
chosen
Wandoor is a coastal village in the Andaman Islands of India, known as a gateway to the Mahatma Gandhi Marine National Park and its surrounding beaches and coral reefs.
-
B.
Udaipura
Udaipura is a town in the central Indian state of Madhya Pradesh, known for its location within the Raisen district and its role as a local administrative and market center.
-
C.
Kukanoor
Kukanoor is a historic village in Karnataka, India, known for its ancient temples and archaeological significance.
-
D.
Shingora
Shingora is a film featuring Indian actress and model Persis Khambatta, known for her distinctive screen presence and international appeal.
-
E.
Vengurla
Vengurla is a coastal town in Maharashtra, India, known for its scenic beaches, historic forts, and cashew plantations.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c4088881908f23f25a82a513f6 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:38 p.m.