Triple
T18540369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalyana Venkateswara Swamy Temple, Narayanavanam |
E453081
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Narayanavanam |
—
|
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: Narayanavanam | Statement: [Kalyana Venkateswara Swamy Temple, Narayanavanam, locatedIn, Narayanavanam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Narayanavanam Context triple: [Kalyana Venkateswara Swamy Temple, Narayanavanam, locatedIn, Narayanavanam]
-
A.
Narayanavanam
chosen
Narayanavanam is a small town in the Tirupati district of Andhra Pradesh, India, known for its historic temples and religious significance.
-
B.
Nallapadu
Nallapadu is a locality and railway junction near Guntur in Andhra Pradesh, India, serving as a regional rail hub.
-
C.
Tadipatri
Tadipatri is a town in the Anantapur district of Andhra Pradesh, India, known for its granite industries and historic temples.
-
D.
Nandipet
Nandipet is a village located in the Nizamabad district of the Indian state of Telangana.
-
E.
Kothapet
Kothapet is a residential and commercial neighborhood in Hyderabad, Telangana, known for its markets, connectivity, and proximity to major city hubs.
- 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e534b64178819082861a03fd067095 |
completed | April 19, 2026, 8:01 p.m. |
Created at: April 10, 2026, 11:37 a.m.