Triple
T9406468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sagaing Region |
E226599
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Tamu |
E756071
|
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: Tamu | Statement: [Sagaing Region, containsCity, Tamu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamu Context triple: [Sagaing Region, containsCity, Tamu]
-
A.
Tamu
chosen
Tamu is a town in northwestern Myanmar’s Sagaing Region, situated near the India–Myanmar border and serving as an important cross-border trade and transit point.
-
B.
Tappitt
Tappitt is a surname associated with the individual referred to as Mr. Tappitt.
-
C.
Baylor
Baylor is a surname most notably associated with American baseball player and manager Don Baylor.
-
D.
Baylor University
Baylor University is a private Christian research university in Waco, Texas, known for its strong academic programs and prominent athletics in the Big 12 Conference.
-
E.
Auburn University
Auburn University is a major public research university in Auburn, Alabama, known for its strong engineering, agriculture, and business programs and its prominent athletics tradition.
- 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_69ca843280488190bc65600e843ef9e6 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd51c3fc988190ac34cc9e09f8ebfc |
completed | April 1, 2026, 5:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1079bd644819081f9c8f25ff1c532 |
completed | April 4, 2026, 12:44 p.m. |
Created at: March 30, 2026, 7:47 p.m.