Triple
T9151351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dolores Costello |
E219590
|
entity |
| Predicate | workedAsChildActor |
P46449
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Dolores Costello, workedAsChildActor, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workedAsChildActor Context triple: [Dolores Costello, workedAsChildActor, true]
-
A.
startedCareerAsChildActor
chosen
Indicates that a person began their professional career in acting during childhood.
-
B.
hasPlayedRole
Indicates that an entity has performed or portrayed a particular role or character in some context (such as a film, play, or production).
-
C.
playedEarlyYearsOf
Indicates that one entity portrayed or acted as the younger/early-life version of another entity in a performance or production.
-
D.
startedActingCareer
Indicates that an entity began their professional work or involvement in acting at a specific time or event.
-
E.
playedRoleIn
Indicates that an entity performed or assumed a specific role or character within a particular event, production, or context.
- 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_69ca83e25418819093c6503deeaf30de |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca96b57888190a09a563f1fa522f6 |
completed | April 1, 2026, 5:13 a.m. |
| PD | Predicate disambiguation | batch_69cc6603ce8c8190bf6e8d6754bdec54 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:20 p.m.