Triple
T19698878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anga |
E473037
|
entity |
| Predicate | associatedWithCity |
P1481
|
FINISHED |
| Object | Munger |
—
|
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: Munger | Statement: [Anga, associatedWithCity, Munger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Munger Context triple: [Anga, associatedWithCity, Munger]
-
A.
Munger
chosen
Munger is a historic city in the eastern Indian state of Bihar, known for its ancient fort, spiritual centers, and traditional gun-making industry.
-
B.
Murry
Murry is a given name and surname that functions as a spelling variant of the more common name Murray.
-
C.
Scaife
Scaife is an American surname most prominently associated with the wealthy and influential Scaife family of industrialists and philanthropists.
-
D.
Milhous
Milhous is the distinctive middle name of Richard Nixon, the 37th president of the United States.
-
E.
Muhney
Muhney is a surname most notably associated with American actor Michael Muhney.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e642b426608190a46abec3652a6ca0 |
completed | April 20, 2026, 3:13 p.m. |
Created at: April 10, 2026, 1:46 p.m.