Triple
T8726741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alba Longa |
E207148
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Remus |
E496789
|
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: Remus | Statement: [Alba Longa, associatedWith, Remus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Remus Context triple: [Alba Longa, associatedWith, Remus]
-
A.
Remus
chosen
Remus is a male given name best known from the Harry Potter character Remus Lupin and the mythological twin of Romulus in Roman legend.
-
B.
Vulpius
Vulpius is a German surname most notably associated with Christiane Vulpius, the longtime companion and later wife of writer Johann Wolfgang von Goethe.
-
C.
Loup
The Loup is a river in southeastern France that flows through the Alpes-Maritimes department, known for its scenic gorges and popular outdoor recreation areas.
-
D.
Cerbère
Cerbère is a coastal commune in southern France near the Spanish border, known for its Mediterranean scenery and winegrowing tradition.
-
E.
Luppi
Luppi is a surname most notably associated with Argentine actor Federico Luppi, renowned for his work in Latin American and Spanish cinema.
- 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_69ca835811d8819081ea00fd2a2c9a1c |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d16cba881908e2a14b60ae65524 |
completed | March 31, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf291b737481909a90e482273c5f76 |
completed | April 3, 2026, 2:42 a.m. |
Created at: March 30, 2026, 6:36 p.m.