Triple
T12225640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Obio-Akpor |
E291343
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Rumuola |
E338072
|
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: Rumuola | Statement: [Obio-Akpor, hasSettlement, Rumuola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rumuola Context triple: [Obio-Akpor, hasSettlement, Rumuola]
-
A.
Rumuola
chosen
Rumuola is a prominent urban neighborhood and transport hub in Port Harcourt, Rivers State, Nigeria.
-
B.
Romão
Romão is a Portuguese given name and surname derived from the Latin name Romanus, commonly associated with Roman heritage.
-
C.
Romana
Romana is a highly intelligent and compassionate Time Lady from the Doctor Who universe who serves as one of the Doctor’s most capable and scholarly companions.
-
D.
Romana
Romana is a small municipality in the Logudoro region of Sardinia, Italy, known for its rural landscape and traditional Sardinian character.
-
E.
Maenza
Maenza is a small historic town in the Lazio region of central Italy, known for its medieval architecture and hilltop setting.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91ca2101c8190955c36704935036a |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e5c1c988190a80917dfe782a6b6 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:51 p.m.