Triple
T19495416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lata Airport |
E487756
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Lata |
—
|
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: Lata | Statement: [Lata Airport, locatedIn, Lata]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lata Context triple: [Lata Airport, locatedIn, Lata]
-
A.
Lata
chosen
Lata is the main town and administrative center of Temotu Province in the eastern Solomon Islands.
-
B.
Lata
Lata is a figure from Hindu tradition known primarily as the wife of the sage Kashyapa.
-
C.
Lata
Lata is the given name of Lata Mangeshkar, the legendary Indian playback singer renowned as the "Nightingale of India."
-
D.
Lasya
Lasya is a graceful, expressive classical Indian dance style traditionally associated with feminine beauty and gentle, fluid movements.
-
E.
Latika
Latika is a central character in the film "Slumdog Millionaire," portrayed as the protagonist's childhood friend and love interest whose life intertwines with his journey from the Mumbai slums to game-show fame.
- 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63490c16481908423e304d82722d7 |
completed | April 20, 2026, 2:13 p.m. |
Created at: April 10, 2026, 1:40 p.m.