Triple
T12310797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sajjangad Fort |
E293471
|
entity |
| Predicate | hasViewOf |
P854
|
FINISHED |
| Object | Satara city |
E58486
|
NE 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: Satara city | Statement: [Sajjangad Fort, hasViewOf, Satara city]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Satara city Context triple: [Sajjangad Fort, hasViewOf, Satara city]
-
A.
Satara
chosen
Satara is a historic city in the Indian state of Maharashtra, known for its role as a key political and cultural center of the Maratha dynasty.
-
B.
Tift
Tift is a surname most notably associated with American politician and Confederate officer Nelson Tift, for whom Tift County, Georgia, is named.
-
C.
Urmond
Urmond is a village in the Dutch province of Limburg, situated along the Juliana Canal near the River Meuse.
-
D.
Titisee-Neustadt
Titisee-Neustadt is a popular resort town in Germany’s Black Forest region, known for its scenic lake Titisee, winter sports facilities, and tourism.
-
E.
Edendale
Edendale is a small rural town in New Zealand’s Southland region, known for its dairy farming and large milk processing facilities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f02c0508190b10c0627cdaaba76 |
completed | April 10, 2026, 6:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6685205608190b504ab6e7c73ee51 |
completed | May 2, 2026, 9:10 p.m. |
Created at: April 8, 2026, 9:53 p.m.