Triple
T32966767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pseudolus |
E843390
|
entity |
| Predicate | hasCourtesanCharacter |
P175447
|
FINISHED |
| Object | Phoenicium |
E2030621
|
NE 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: Phoenicium | Statement: [Pseudolus, hasCourtesanCharacter, Phoenicium]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCourtesanCharacter Context triple: [Pseudolus, hasCourtesanCharacter, Phoenicium]
-
A.
hasFemmeFataleCharacter
Indicates that a work includes a femme fatale character who plays a significant role in the narrative.
-
B.
hasPimpCharacter
chosen
Indicates that one entity plays the role of a pimp in relation to another entity.
-
C.
hasPatronCharacter
Indicates that one entity serves as a patron, protector, or guiding figure for another entity.
-
D.
hasYoungLoverCharacter
Indicates that an entity is involved in a romantic or intimate relationship with a significantly younger lover character.
-
E.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
- F. None of above.
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_69f3494af2808190ad98cec2f1bc0fe6 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a34dab9b50c81908eab5e3d23d12ecc |
completed | June 19, 2026, 5:59 a.m. |
| PD | Predicate disambiguation | batch_6a0379f0cbe481909b4b8fc6cbe297f0 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:21 a.m.