Triple
T35134617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rome Metro Line A |
E1014534
|
entity |
| Predicate | isDiagonalAcrossCity |
P130287
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Rome Metro Line A, isDiagonalAcrossCity, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isDiagonalAcrossCity Context triple: [Rome Metro Line A, isDiagonalAcrossCity, true]
-
A.
crossesInCity
Indicates that one entity crosses or passes through another entity within the boundaries of a specified city.
-
B.
runsDiagonally
chosen
Indicates that one entity extends or moves in a diagonal direction relative to another reference frame or axis.
-
C.
isCrosstown
Indicates that something (such as a route, trip, or connection) runs across a town or city, typically linking areas on opposite sides without passing through the central hub.
-
D.
diagonalProperty
Indicates a relationship where something possesses or exhibits a diagonal characteristic, alignment, or behavior relative to a reference frame or structure.
-
E.
hasRelativePositionInCity
Indicates that one entity occupies a specific spatial or positional relationship within the boundaries or layout of a particular city.
- 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_69f76dd9c1848190af70d4882a2c1ad7 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78c6d6b9881909ccd12d8e2e6639e |
completed | May 3, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69f78b9106008190930b3b3675b737d6 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4:02 p.m.