Triple
T1350801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayan languages |
E28875
|
entity |
| Predicate | includesLanguage |
P2177
|
FINISHED |
| Object | Mam |
E86612
|
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: Mam | Statement: [Mayan languages, includesLanguage, Mam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mam Context triple: [Mayan languages, includesLanguage, Mam]
-
A.
Mam
chosen
Mam is a Mayan language spoken primarily by the Mam people in the western highlands of Guatemala and parts of southern Mexico.
-
B.
MAMAC
MAMAC is a modern and contemporary art museum in Nice, France, known for its collections of postwar European and American art.
-
C.
Mama
"Mama" is a 1987 debut novel by Terry McMillan that follows a resilient Black single mother struggling to raise her children and rebuild her life amid poverty and personal turmoil.
-
D.
Ma
Ma is a common Chinese surname borne by many notable individuals across fields such as music, politics, and sports.
-
E.
Mom
Mom is a popular American sitcom starring Allison Janney and Anna Faris that follows a dysfunctional mother-daughter duo in recovery from addiction.
- 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_69a498571d248190a0ac9eb02d97097f |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c26981d081909ca3b8d8cdf7cf2e |
completed | March 1, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acc63eef908190aef058396f63a5a4 |
completed | March 8, 2026, 12:43 a.m. |
Created at: March 1, 2026, 7:56 p.m.