Triple
T6247497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Second Mesa |
E139754
|
entity |
| Predicate | hasVillage |
P4011
|
FINISHED |
| Object | Mishongnovi |
E149486
|
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: Mishongnovi | Statement: [Second Mesa, hasVillage, Mishongnovi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mishongnovi Context triple: [Second Mesa, hasVillage, Mishongnovi]
-
A.
Mishongnovi
chosen
Mishongnovi is a traditional Hopi village in northeastern Arizona, known as one of the oldest continuously inhabited settlements in North America.
-
B.
Monywa
Monywa is a significant commercial and transport hub in central Myanmar, known for its trade, agriculture, and nearby Buddhist sites.
-
C.
Mikongo
Mikongo is a small settlement in central Gabon that serves as a key access point for visitors exploring Lope National Park.
-
D.
Muyil
Muyil is an ancient Maya archaeological site in Mexico’s Yucatán Peninsula, known for its well-preserved temples and proximity to the Sian Ka'an Biosphere Reserve.
-
E.
Mangini
Mangini is an Italian surname most notably associated with former NFL head coach and analyst Eric Mangini.
- 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_69c008b1c5088190ae6de2555fc05ad8 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0631f511c81908d413320efdce42e |
completed | March 22, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5191599d0819098a1b20b9d680f4b |
completed | March 26, 2026, 11:31 a.m. |
Created at: March 22, 2026, 4:23 p.m.