Triple
T1778259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nola |
E39229
|
entity |
| Predicate | hasAncientName |
P20952
|
FINISHED |
| Object | Nola |
E39229
|
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: Nola | Statement: [Nola, hasAncientName, Nola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nola Context triple: [Nola, hasAncientName, Nola]
-
A.
Nola
chosen
Nola is an ancient town in southern Italy, historically significant in Roman times and known as the place where Emperor Augustus died.
-
B.
New Orleans
New Orleans is a historic port city in southeastern Louisiana known for its vibrant jazz music, Creole cuisine, and distinctive French and Spanish-influenced architecture.
-
C.
Shreveport
Shreveport is a major city in northwestern Louisiana known for its role as a regional commercial, cultural, and transportation hub.
-
D.
Biloxi
Biloxi is a coastal Mississippi city known for its beaches, casinos, and seafood industry along the Gulf of Mexico.
-
E.
Lafayette, Louisiana
Lafayette, Louisiana is a mid-sized city in south-central Louisiana known as the heart of Cajun and Creole culture, with a vibrant music, food, and festival scene.
- 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_69a88630519c8190a17addd83c4a3ef4 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa64ba92a48190b69da748dfbfc53c |
completed | March 6, 2026, 5:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc01252ec8190a14ff51151d8e69e |
completed | March 10, 2026, 6:54 a.m. |
Created at: March 4, 2026, 7:31 p.m.