Triple
T31656310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New New York City |
E807865
|
entity |
| Predicate | hasFictionalGovernmentBody |
P189332
|
FINISHED |
| Object | Earth President's office |
—
|
NE NERFINISHED |
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: Earth President's office | Statement: [New New York City, hasFictionalGovernmentBody, Earth President's office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalGovernmentBody Context triple: [New New York City, hasFictionalGovernmentBody, Earth President's office]
-
A.
hasFictionalGovernmentAgency
Indicates that an entity includes, features, or is associated with a government agency that is fictional rather than real.
-
B.
hasGovernmentBody
Indicates that one entity serves as the official governing or administrative body responsible for overseeing or managing another entity.
-
C.
hasFictionalAdministration
Indicates that an entity is governed, managed, or overseen by an administration that is fictional rather than real.
-
D.
hasFictionalOffice
chosen
Indicates that one entity maintains or is associated with an office or workplace that exists only in a fictional or imaginary context.
-
E.
hasFictionalLeader
Indicates that an entity is led or governed by a leader who is a fictional character rather than a real person.
- 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_69f348daf95c81908b4c985b7ddcd0b3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe8f74748c8190bd14a856c057f9f7 |
completed | May 9, 2026, 1:35 a.m. |
| PD | Predicate disambiguation | batch_69fe8e7ed8088190929e0df67aca4de9 |
completed | May 9, 2026, 1:31 a.m. |
Created at: April 30, 2026, 10:55 p.m.