Triple
T7514353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valens Aqueduct |
E177601
|
entity |
| Predicate | numberOfArcades |
P24796
|
FINISHED |
| Object | two-tier arcade |
—
|
LITERAL 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: two-tier arcade | Statement: [Valens Aqueduct, numberOfArcades, two-tier arcade]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfArcades Context triple: [Valens Aqueduct, numberOfArcades, two-tier arcade]
-
A.
hasArcades
chosen
Indicates that one entity features or contains arcaded structures (a series of arches or covered passageways) associated with another entity.
-
B.
numberOfSlotMachines
Indicates the quantity of slot machines associated with a given entity or location.
-
C.
upperDeckLanes
Indicates that the related entities are lanes located on the upper deck level of a multi-level structure (such as a roadway, bridge, or parking facility).
-
D.
numberOfAttractions
Indicates the total count of attractions associated with a given entity or context.
-
E.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
- F. None of above.
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_69c69f2891148190a484f3b8222c6f1b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5d6ccb08190a568a9b58bfbd0cc |
completed | March 27, 2026, 9:25 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d44e9481909813e073b194f6f4 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:45 p.m.