Triple
T452018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mother Teresa |
E7150
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Anjezë
Anjezë is the birth name of Mother Teresa, the Catholic nun and missionary renowned for her humanitarian work among the poor in Kolkata, India.
|
E56701
|
NE FINISHED |
How this triple was built (4 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: Anjezë | Statement: [Mother Teresa, givenName, Anjezë]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anjezë Context triple: [Mother Teresa, givenName, Anjezë]
-
A.
Tosk
Tosk is the southern variety of Albanian that forms the basis of the standard Albanian language.
-
B.
Marić
Marić is the Serbian family name of Mileva Marić, a pioneering physicist and mathematician known for her association with Albert Einstein.
-
C.
Hazaragi
Hazaragi is a variety of Persian primarily spoken by the Hazara people of central Afghanistan and surrounding regions, distinguished by its unique phonology and significant Turkic and Mongolic influences.
-
D.
Brega
Brega is a strategic coastal town in northeastern Libya known for its major oil facilities and its role as a key battleground during the 2011 Libyan Civil War.
-
E.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Anjezë Triple: [Mother Teresa, givenName, Anjezë]
Generated description
Anjezë is the birth name of Mother Teresa, the Catholic nun and missionary renowned for her humanitarian work among the poor in Kolkata, India.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anjezë Target entity description: Anjezë is the birth name of Mother Teresa, the Catholic nun and missionary renowned for her humanitarian work among the poor in Kolkata, India.
-
A.
Tosk
Tosk is the southern variety of Albanian that forms the basis of the standard Albanian language.
-
B.
Marić
Marić is the Serbian family name of Mileva Marić, a pioneering physicist and mathematician known for her association with Albert Einstein.
-
C.
Hazaragi
Hazaragi is a variety of Persian primarily spoken by the Hazara people of central Afghanistan and surrounding regions, distinguished by its unique phonology and significant Turkic and Mongolic influences.
-
D.
Brega
Brega is a strategic coastal town in northeastern Libya known for its major oil facilities and its role as a key battleground during the 2011 Libyan Civil War.
-
E.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
- F. None of above. chosen
Provenance (5 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_69a2e7e4676c81909ea0dbdecac0687c |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef854f7481909dc2207faf0327ec |
completed | Feb. 28, 2026, 1:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a44802e858819081a0b5b98bb25bce |
completed | March 1, 2026, 2:06 p.m. |
| NEDg | Description generation | batch_69a449b1e2708190838e32497ffd2fbd |
completed | March 1, 2026, 2:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a44a07fa18819089cb8005d476d078 |
completed | March 1, 2026, 2:15 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.