Triple
T5110682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1960s Los Angeles |
E115205
|
entity |
| Predicate | transportationModeDominant |
P1379
|
FINISHED |
| Object | automobile |
—
|
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: automobile | Statement: [1960s Los Angeles, transportationModeDominant, automobile]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transportationModeDominant Context triple: [1960s Los Angeles, transportationModeDominant, automobile]
-
A.
transportModeFamily
Indicates the general category or family of transportation mode to which a specific transport mode belongs (e.g., road, rail, air, water).
-
B.
primaryTransportModel
Indicates that one transport model is designated as the main or default model used for a given context or entity.
-
C.
publicTransitMode
Indicates the type of public transportation (e.g., bus, train, subway) used or associated with a given trip or segment.
-
D.
transportType
chosen
Indicates the mode or means of transportation used in carrying something or someone from one place to another.
-
E.
hasPublicTransitMode
Indicates that a location, route, or service is associated with or supports a specific mode of public transportation (e.g., bus, train, tram).
- 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_69bd4441d1648190a54a533895041987 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd75ad362c8190b9cbded390aaea3c |
completed | March 20, 2026, 4:28 p.m. |
| PD | Predicate disambiguation | batch_69bd715fe3a8819087d3065adddba515 |
completed | March 20, 2026, 4:10 p.m. |
Created at: March 20, 2026, 1:41 p.m.