Triple
T9854397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vision Quest |
E239547
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Carla |
E101410
|
NE 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: Carla | Statement: [Vision Quest, featuresCharacter, Carla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carla Context triple: [Vision Quest, featuresCharacter, Carla]
-
A.
Carla
chosen
Carla is a feminine given name commonly used in various languages, often considered the female form of Carl or Charles.
-
B.
Carly
Carly is a feminine given name most famously associated with American singer-songwriter Carly Simon.
-
C.
Carole
Carole is a feminine given name of French origin, commonly used in English-speaking countries.
-
D.
Carine
Carine is a feminine given name, often considered a variant of names like Catherine or Karine, used in various European languages.
-
E.
Karin
Karin is a feminine given name used in various cultures, often considered a variant of names like Karen or Katherine.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3960fb481909c90d6d6cafc6222 |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1ead91ee88190bd4cb28ab16bf703 |
completed | April 5, 2026, 4:53 a.m. |
Created at: March 30, 2026, 8:34 p.m.