Triple
T38297857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Chaussee Spit |
E1032140
|
entity |
| Predicate | harborCharacter |
P190615
|
FINISHED |
| Object | treacherous |
—
|
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: treacherous | Statement: [La Chaussee Spit, harborCharacter, treacherous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: harborCharacter Context triple: [La Chaussee Spit, harborCharacter, treacherous]
-
A.
crewDepicted
Indicates that a particular crew or group of crew members is visually represented or shown in some form of media or depiction.
-
B.
eraCharacter
Indicates that a character is associated with, or belongs to, a particular historical or fictional era.
-
C.
hatCharakter
Indicates that an entity possesses or exhibits a particular character, trait, or quality.
-
D.
franchiseCharacter
Indicates a relationship where a character belongs to, appears in, or is part of a particular media franchise.
-
E.
brandCharacter
Indicates that one entity serves as a brand character or mascot representing another entity (typically a brand or product).
- 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_69f76e0f2084819091299d021625c3fe |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fccbd826708190b5fab12c4236299a |
completed | May 7, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69fcc58838e08190b8fa54aa5c165f2d |
completed | May 7, 2026, 5:02 p.m. |
| PDg | Predicate description generation | batch_69fccbd6b7688190b746803cf78d5704 |
completed | May 7, 2026, 5:28 p.m. |
Created at: May 3, 2026, 4:30 p.m.