Triple
T20969150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Brand |
E516448
|
entity |
| Predicate | bookCounterpart |
P134199
|
FINISHED |
| Object | composite of real-life analysts described in Moneyball |
—
|
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: composite of real-life analysts described in Moneyball | Statement: [Peter Brand, bookCounterpart, composite of real-life analysts described in Moneyball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bookCounterpart Context triple: [Peter Brand, bookCounterpart, composite of real-life analysts described in Moneyball]
-
A.
novelCounterpart
Indicates a relationship where one entity serves as a new or innovative counterpart or alternative to another.
-
B.
book
Indicates that an agent reserves or schedules a service, event, or resource for future use.
-
C.
seLibróEntre
Indicates that something or someone was freed or released amid, or in the context of, something else (e.g., a situation, group, or environment).
-
D.
book1Contains
Indicates that one book includes, encloses, or has as part of its content another specified element or section.
-
E.
bookContext
chosen
Indicates that something is relevant to, derived from, or situated within the context or setting of a particular book.
- 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_69e0b4fee5ac8190875fa9ceba1a5e5e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fb9e3a50819085b3a974bda812e0 |
completed | April 21, 2026, 4:22 a.m. |
| PD | Predicate disambiguation | batch_69e5dbe6976081908abd4e9c8734bae9 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 1:42 p.m.