Triple
T4399743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harry Burns |
E84774
|
entity |
| Predicate | hasMemorableScene |
P7326
|
FINISHED |
| Object | diner conversation about relationships |
—
|
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: diner conversation about relationships | Statement: [Harry Burns, hasMemorableScene, diner conversation about relationships]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMemorableScene Context triple: [Harry Burns, hasMemorableScene, diner conversation about relationships]
-
A.
hasMemorableMoments
Indicates that something contains or is associated with particularly notable or unforgettable moments or events.
-
B.
notableScene
chosen
Indicates that a particular scene is especially significant, memorable, or noteworthy within a work or context.
-
C.
hasDramaticElements
Indicates that something contains features or qualities characteristic of drama, such as heightened emotion, tension, or conflict.
-
D.
hasMemorial
Indicates that a memorial exists in honor of, or dedicated to, a particular entity.
-
E.
hasEnigmaticCharacter
Indicates that something possesses a mysterious, puzzling, or difficult-to-interpret quality or nature.
- 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.