Triple
T575746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Me at the zoo |
E13757
|
entity |
| Predicate | hasOpeningLine |
P829
|
FINISHED |
| Object | All right, so here we are in front of the elephants. |
—
|
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: All right, so here we are in front of the elephants. | Statement: [Me at the zoo, hasOpeningLine, All right, so here we are in front of the elephants.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOpeningLine Context triple: [Me at the zoo, hasOpeningLine, All right, so here we are in front of the elephants.]
-
A.
openingLine
chosen
Indicates that one entity is the first line or initial statement that begins another entity, such as a text, speech, or conversation.
-
B.
hasOpeningLyric
Indicates that one entity serves as the opening lyric of another entity, typically a song or musical work.
-
C.
hasTextOpening
Indicates that an entity begins with or contains a specified initial segment of text.
-
D.
openedIn
Indicates that an entity (such as a business, event, or institution) began operating or was inaugurated in a specific time period or location.
-
E.
hasIconicLine
Indicates that an entity (such as a work or character) is associated with a particularly famous or memorable line of dialogue.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b67395c8190a8046ff7debe9d1f |
completed | March 1, 2026, 8:02 p.m. |
| PD | Predicate disambiguation | batch_69a494c692288190b88f30299516b5ba |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.