Triple
T24300458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Damson Idris |
E606079
|
entity |
| Predicate | characterSettingOfFranklinSaint |
P90820
|
FINISHED |
| Object | Los Angeles |
—
|
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: Los Angeles | Statement: [Damson Idris, characterSettingOfFranklinSaint, Los Angeles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterSettingOfFranklinSaint Context triple: [Damson Idris, characterSettingOfFranklinSaint, Los Angeles]
-
A.
characterSetting
chosen
Indicates that a character is associated with, appears in, or is situated within a particular setting or environment.
-
B.
storyCharacterizedAs
Indicates that a story is described, portrayed, or defined as having a particular quality, style, or attribute.
-
C.
characterInWorkDescribedAs
Indicates that a character is portrayed or described in a particular way within a specific work.
-
D.
cityQuarterCharacter
Indicates the characteristic qualities or distinctive nature that define a particular city quarter.
-
E.
characterDescription
Indicates that one entity provides a textual description or portrayal of the characteristics, traits, or attributes of another entity.
- 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_69e29549335881909cbf27adcaba1cf0 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f2915e4ffc8190bf711dae443b3ec1 |
completed | April 29, 2026, 11:16 p.m. |
| PD | Predicate disambiguation | batch_69f1c45c6ec081908401b69424428100 |
completed | April 29, 2026, 8:42 a.m. |
Created at: April 18, 2026, 12:09 a.m.