Triple
T20150347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hennessy Street |
E491417
|
entity |
| Predicate | canPortray |
P27589
|
FINISHED |
| Object | various U.S. city streets |
—
|
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: various U.S. city streets | Statement: [Hennessy Street, canPortray, various U.S. city streets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canPortray Context triple: [Hennessy Street, canPortray, various U.S. city streets]
-
A.
portraysPersonAs
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
B.
canBeDepictedAs
chosen
Indicates that one entity is capable of being visually represented or illustrated in the form or style of another entity.
-
C.
portrayalRecognition
Indicates that one entity recognizes or identifies another entity as a portrayal or representation of a particular subject or character.
-
D.
portrayalFeature
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
E.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667a1c5848190975b17ab07251f8b |
completed | April 20, 2026, 5:51 p.m. |
| PD | Predicate disambiguation | batch_69e54cfd924881909b55f3e4d3e7e070 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:33 p.m.