Triple
T25873525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caesar (The Twa Dogs) |
E651824
|
entity |
| Predicate | numberOfProtagonistsInWork |
P29044
|
FINISHED |
| Object | two |
—
|
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: two | Statement: [Caesar (The Twa Dogs), numberOfProtagonistsInWork, two]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfProtagonistsInWork Context triple: [Caesar (The Twa Dogs), numberOfProtagonistsInWork, two]
-
A.
protagonistCount
chosen
Indicates the number of primary protagonists involved in a given narrative or work.
-
B.
numberOfHumanProtagonists
Indicates the count of human characters that serve as protagonists in a given work or context.
-
C.
hasHumanProtagonists
Indicates that the primary characters driving the narrative are human beings rather than non-human entities.
-
D.
numberOfHumanCharacters
Indicates the count of distinct human characters associated with a given entity or context.
-
E.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
- 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_69e7ab3ad9d88190841ddcb93ab02e96 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69fd09840ea88190a2e6d7e577ade717 |
completed | May 7, 2026, 9:52 p.m. |
| PD | Predicate disambiguation | batch_69fd064c49988190afadddbd04d7cb94 |
completed | May 7, 2026, 9:38 p.m. |
Created at: April 22, 2026, 8:12 a.m.