Triple
T9031802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lotha Naga |
E216389
|
entity |
| Predicate | ethnicity |
P194
|
FINISHED |
| Object | Naga |
E473888
|
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: Naga | Statement: [Lotha Naga, ethnicity, Naga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naga Context triple: [Lotha Naga, ethnicity, Naga]
-
A.
Naga
Naga is a city in the Bicol Region of the Philippines known as a major religious, cultural, and commercial center, particularly famed for the annual Peñafrancia Festival.
-
B.
Naga
Naga is a coastal city and municipality on Cebu Island in the Philippines known for its industrial activities and growing urban development.
-
C.
Naga
chosen
Naga refers to a mythological serpent or dragon-like being found in Hindu and Buddhist traditions, often associated with water, protection, and hidden wisdom.
-
D.
Lunglei
Lunglei is a major town in the Indian state of Mizoram and an important cultural and administrative center for the Mizo people.
-
E.
Yaka
Yaka is a major Bantu language spoken primarily in the Democratic Republic of the Congo and neighboring regions of Central Africa.
- 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_69ca83d10b608190b2b2f8e0a7faaf14 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6a9f2c7481909b4a272183f20585 |
completed | April 1, 2026, 12:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdbc662208190a3f4e6e593208c5c |
completed | April 3, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:08 p.m.