Triple
T10732565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bunker Hill, Los Angeles |
E253109
|
entity |
| Predicate | formerCharacter |
P95686
|
FINISHED |
| Object | residential neighborhood with Victorian houses |
—
|
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: residential neighborhood with Victorian houses | Statement: [Bunker Hill, Los Angeles, formerCharacter, residential neighborhood with Victorian houses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerCharacter Context triple: [Bunker Hill, Los Angeles, formerCharacter, residential neighborhood with Victorian houses]
-
A.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
B.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
C.
fictionalCharacter
Indicates that one entity is a fictional character that appears within the narrative world of another entity (such as a work, series, or franchise).
-
D.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
E.
eraCharacter
Indicates that a character is associated with, or belongs to, a particular historical or fictional era.
- 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_69d6aa5d8be481909a43218b2bfdbe95 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d7101ff9808190a27fcc06da097ea3 |
completed | April 9, 2026, 2:34 a.m. |
| PD | Predicate disambiguation | batch_69d6f309a44881908e49e3ba478c35b4 |
completed | April 9, 2026, 12:30 a.m. |
| PDg | Predicate description generation | batch_69d6fa323564819097b207eb53f8a9b8 |
completed | April 9, 2026, 1 a.m. |
Created at: April 8, 2026, 9:14 p.m.