Triple
T20704871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinatown (1974 film) |
E508876
|
entity |
| Predicate | followsCharacterProfession |
P141167
|
FINISHED |
| Object | private investigator |
—
|
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: private investigator | Statement: [Chinatown (1974 film), followsCharacterProfession, private investigator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: followsCharacterProfession Context triple: [Chinatown (1974 film), followsCharacterProfession, private investigator]
-
A.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
B.
followsCharacter
Indicates that one character moves or acts after another character, maintaining a trailing or subsequent position or sequence relative to them.
-
C.
followsCharacterWhoIs
Indicates that one character consistently trails, pursues, or comes after another character who has a specified property or role.
-
D.
followsCharacterFrom
Indicates that one character moves or proceeds behind another character, maintaining a trailing or pursuing position relative to them.
-
E.
followsCharactersFrom
Indicates that one entity continues or tracks the narrative, actions, or developments involving specific characters from another entity.
- F. None of above. chosen
Provenance (4 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_69e0b4c2b2a481909e31e9cb8f81ab55 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c18ea874819092a125d9f929a311 |
completed | April 21, 2026, 12:15 a.m. |
| PD | Predicate disambiguation | batch_69e5c044d1108190b2b5d25de23f6401 |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3caef50819093c8159fe8d6435b |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 12:13 p.m.