Triple
T15200961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agen aqueduct |
E363264
|
entity |
| Predicate | hasViewOf |
P854
|
FINISHED |
| Object | Agen |
E64826
|
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: Agen | Statement: [Agen aqueduct, hasViewOf, Agen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Agen Context triple: [Agen aqueduct, hasViewOf, Agen]
-
A.
Agen
chosen
Agen is a historic town in southwestern France known for its prunes and location between Bordeaux and Toulouse.
-
B.
Agen canton
Agen canton is an administrative division in the Lot-et-Garonne department of southwestern France, centered around the city of Agen.
-
C.
Argeo
Argeo is the given first name of Paul Cellucci, an American politician and former Governor of Massachusetts.
-
D.
Agaja
Agaja was an 18th-century king of the Kingdom of Dahomey in West Africa, known for expanding the kingdom’s power and centralizing its political and military structures.
-
E.
Ageo
Ageo is a city in Japan known as a residential and industrial hub within the Greater Tokyo metropolitan area.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006b588b88190a88e91d521acbdfe |
completed | April 15, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5e82400819087dc782c1f48af39 |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 10, 2026, 3:10 a.m.