Triple
T38125044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anja Spiegelman |
E952042
|
entity |
| Predicate | portrayedAsSpecies |
P193353
|
FINISHED |
| Object | mouse |
—
|
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: mouse | Statement: [Anja Spiegelman, portrayedAsSpecies, mouse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedAsSpecies Context triple: [Anja Spiegelman, portrayedAsSpecies, mouse]
-
A.
characterSpeciesPortrayed
chosen
Indicates that a character is portrayed as belonging to a particular species.
-
B.
portrayedBySpeciesActor
Indicates that an entity is depicted or represented by an actor belonging to a particular species.
-
C.
associatedCharacterSpecies
Indicates that one entity is related to, or linked with, the species of a particular character.
-
D.
appearsWithSpecies
Indicates that one entity is observed or recorded together with a particular species in the same context or occurrence.
-
E.
describedAsSpecies
Indicates that one entity is characterized or identified as a particular species in relation to 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_69f76f083548819082bd2bbf53c79e8e |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ffb82be8148190a1c870d467a28c80 |
completed | May 9, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69ffb7bbd550819094052e9a0d0ae320 |
completed | May 9, 2026, 10:39 p.m. |
Created at: May 3, 2026, 4:21 p.m.