Triple
T1722244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angelina Jolie |
E37415
|
entity |
| Predicate | hasTattoo |
P31179
|
FINISHED |
| Object | multiple tattoos |
—
|
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: multiple tattoos | Statement: [Angelina Jolie, hasTattoo, multiple tattoos]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTattoo Context triple: [Angelina Jolie, hasTattoo, multiple tattoos]
-
A.
hasLie
Indicates that an entity is associated with or responsible for a specific lie or false statement.
-
B.
hasSpray
Indicates that one entity possesses, contains, or is equipped with a spray or spraying capability in relation to another entity or context.
-
C.
hasInsigniaWornBy
Indicates that a particular insignia is worn by a specified entity (such as a person, group, or organization).
-
D.
hasTissue
Indicates that one entity possesses, contains, or is associated with a specific tissue of another entity.
-
E.
hasDesign
Indicates that one entity possesses, embodies, or is characterized by a particular design associated with another entity.
- 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_69a8861acab88190bb43cde203429399 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aadb7bda1081908f2c41c520c9c55c |
completed | March 6, 2026, 1:49 p.m. |
| PD | Predicate disambiguation | batch_69aa61c0a0288190bce9d60062a84b69 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69aadb68868c819097ec6db6194abae6 |
completed | March 6, 2026, 1:49 p.m. |
Created at: March 4, 2026, 7:30 p.m.