Triple
T29706556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Pursuit of Happyness |
E751645
|
entity |
| Predicate | hasMainCharacterBasedOn |
P24629
|
FINISHED |
| Object | Chris Gardner |
—
|
NE NERFINISHED |
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: Chris Gardner | Statement: [The Pursuit of Happyness, hasMainCharacterBasedOn, Chris Gardner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainCharacterBasedOn Context triple: [The Pursuit of Happyness, hasMainCharacterBasedOn, Chris Gardner]
-
A.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
-
B.
hasMainThemeCharacter
Indicates that a work (such as a story, film, or game) features a specific character as its central or primary thematic focus.
-
C.
protagonistBasedOn
chosen
Indicates that a fictional work’s main character is modeled on, inspired by, or derived from a particular real or fictional person or entity.
-
D.
hasMainCharacterDiscussed
Indicates that the main character has engaged in a discussion or conversation about a particular subject or with another entity.
-
E.
laterMainCharacterOf
Indicates that one entity becomes the main character of a work at a later point in time, succeeding another main character.
- 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_69f0d62748848190b030d0a703629a7d |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69ff6c061c6c81909ff485e9cafc88a2 |
completed | May 9, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69ff6aaf886c8190a3c87d089453f3de |
completed | May 9, 2026, 5:11 p.m. |
Created at: April 28, 2026, 7:27 p.m.