Triple
T4399737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harry Burns |
E84774
|
entity |
| Predicate | meetsCharacterDuring |
P3123
|
FINISHED |
| Object | car trip from Chicago to New York City |
—
|
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: car trip from Chicago to New York City | Statement: [Harry Burns, meetsCharacterDuring, car trip from Chicago to New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meetsCharacterDuring Context triple: [Harry Burns, meetsCharacterDuring, car trip from Chicago to New York City]
-
A.
meetsAs
Indicates that two entities encounter or come together at the same place and time, typically in a planned or recognized interaction.
-
B.
meetsDuring
chosen
Indicates that one entity encounters or comes together with another while a specified event or time interval is in progress.
-
C.
meetsBetween
Indicates that one entity meets or encounters another at some point between two specified reference points or times.
-
D.
meets
Indicates that two or more entities come together at the same place and time, typically for interaction or a shared purpose.
-
E.
meetsEvery
Indicates that one entity encounters or comes into contact with every member of a specified set of entities.
- 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_69b345158c748190a2c040fce2da9980 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b352cc4ab081908bc45d2f76cd4da8 |
completed | March 12, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69b34f597998819092477efdedb51427 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:27 p.m.