Triple
T3869712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Street (Alexandria, Virginia) |
E91952
|
entity |
| Predicate | hasStreetcarHistory |
P51576
|
FINISHED |
| Object | former streetcar route |
—
|
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: former streetcar route | Statement: [King Street (Alexandria, Virginia), hasStreetcarHistory, former streetcar route]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStreetcarHistory Context triple: [King Street (Alexandria, Virginia), hasStreetcarHistory, former streetcar route]
-
A.
hasStreetcarSystem
Indicates that a place possesses or is served by an operational streetcar (tram) transit system.
-
B.
hasStreetcarLoop
Indicates that a location or facility includes a dedicated looped track or route specifically for streetcars to turn around or circulate.
-
C.
hasTramway
Indicates that a location or area is served by, contains, or is connected to a tramway system.
-
D.
hasHistoricStreet
Indicates that an entity is associated with or located on a street that has recognized historical significance.
-
E.
hasRailroadHistory
Indicates that an entity is associated with, involved in, or notable for historical events, operations, or developments related to railroads.
- F. None of above. chosen
Provenance (4 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_69aed9645f348190a9868e7cef56ab7e |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec533828819080f52dae15fdbecd |
completed | March 9, 2026, 3:50 p.m. |
| PD | Predicate disambiguation | batch_69aee754dddc8190936e1f9c40a770db |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aee80858a481909961a33fb50ff8d1 |
completed | March 9, 2026, 3:32 p.m. |
Created at: March 9, 2026, 3:20 p.m.