Triple
T13503226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lefty O'Doul |
E320943
|
entity |
| Predicate | hasSignatureRestaurant |
P4442
|
FINISHED |
| Object | Lefty O'Doul's restaurant in San Francisco |
—
|
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: Lefty O'Doul's restaurant in San Francisco | Statement: [Lefty O'Doul, hasSignatureRestaurant, Lefty O'Doul's restaurant in San Francisco]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSignatureRestaurant Context triple: [Lefty O'Doul, hasSignatureRestaurant, Lefty O'Doul's restaurant in San Francisco]
-
A.
hasRestaurant
chosen
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
-
B.
hasRestaurantType
Indicates that an entity is associated with or classified as a particular type or category of restaurant.
-
C.
isDiningDestination
Indicates that a place serves as a destination where people go specifically to eat meals or dine.
-
D.
hasCharacterDining
Indicates that an entity offers or includes dining experiences where guests can eat while interacting with costumed characters.
-
E.
hasSignatureBuilding
Indicates that an entity possesses or is associated with a distinctive, emblematic building that serves as its primary or most recognizable physical structure.
- 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf810e248190a060481004503f96 |
completed | April 12, 2026, 2:43 p.m. |
| PD | Predicate disambiguation | batch_69dbae0b63748190b5e207f84b2532ea |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:43 p.m.