Triple
T31282748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houston Brooks |
E797717
|
entity |
| Predicate | employerCharacteristic |
P179520
|
FINISHED |
| Object | Monarch is a covert agency |
—
|
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: Monarch is a covert agency | Statement: [Houston Brooks, employerCharacteristic, Monarch is a covert agency]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerCharacteristic Context triple: [Houston Brooks, employerCharacteristic, Monarch is a covert agency]
-
A.
employmentCharacteristic
Indicates a specific attribute, condition, or quality associated with a person’s employment or job situation.
-
B.
employerNumberCharacteristic
Indicates a relationship where an entity is associated with a specific numeric characteristic that quantifies or identifies an employer.
-
C.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
D.
domainCharacteristic
Indicates that a domain or area of interest possesses a particular characteristic or defining property.
-
E.
employerFocus
Indicates that an employer directs particular attention, resources, or priority toward a specific subject, group, or area.
- F. None of above. chosen
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_69f224def9088190a37034eab3daf57f |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f722c1bc648190a79bfdc722dcaaa4 |
completed | May 3, 2026, 10:26 a.m. |
| PD | Predicate disambiguation | batch_69f72153a9188190b02adc84e1be4af8 |
completed | May 3, 2026, 10:20 a.m. |
| PDg | Predicate description generation | batch_69f7221bc57c819085c1464a45e61b2f |
completed | May 3, 2026, 10:23 a.m. |
Created at: April 29, 2026, 9:13 p.m.