Triple
T3066910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hector Barbossa |
E62123
|
entity |
| Predicate | statusInFirstFilm |
P44209
|
FINISHED |
| Object | undead pirate |
—
|
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: undead pirate | Statement: [Hector Barbossa, statusInFirstFilm, undead pirate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statusInFirstFilm Context triple: [Hector Barbossa, statusInFirstFilm, undead pirate]
-
A.
firstAVA
Indicates that the subject is the first entity (e.g., version, appearance, or instance) in a sequence associated with the object.
-
B.
firstAwardedForFilm
Indicates the film for which an entity (such as a person or award) was first given or received an award.
-
C.
firstFilmTitle
Indicates the title of the first film associated with a given entity.
-
D.
firstSawAction
Indicates that one entity was the earliest or initial observer of a particular action performed by another entity.
-
E.
includedInFilm
Indicates that one entity (such as a scene, segment, or element) is contained within or forms part of a particular film.
- 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0fd87308190918e7b616f033faa |
completed | March 8, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69ad9624b7a0819091d255614f5819ea |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f7630c81908e1ca8a69611cff6 |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 3:02 p.m.