Triple
T8501378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Go Set a Watchman |
E201224
|
entity |
| Predicate | portraysScoutAs |
P13483
|
FINISHED |
| Object | adult woman living in New York City |
—
|
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: adult woman living in New York City | Statement: [Go Set a Watchman, portraysScoutAs, adult woman living in New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysScoutAs Context triple: [Go Set a Watchman, portraysScoutAs, adult woman living in New York City]
-
A.
portraysAgeGroup
chosen
Indicates that one entity depicts or represents another entity as belonging to a particular age group.
-
B.
scoutedFor
Indicates that one entity has been evaluated or searched out as a potential candidate, resource, or opportunity on behalf of another entity.
-
C.
portraysAsVictim
Indicates that one entity represents or depicts another entity as a victim in a given context or narrative.
-
D.
portraysAdversary
Indicates that one entity depicts or represents another entity as an opponent, enemy, or rival.
-
E.
portraysYoungerVersionOfCharacterFrom
Indicates that one character is depicted as a younger version of another character from a specified source.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe59ad65c8190a2b8e6d22269853a |
completed | March 31, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cbd10a4b0881909e254117780dc823 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:14 p.m.