Triple
T177057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spanish missions in California |
E3595
|
entity |
| Predicate | numberOfMissions |
P7449
|
FINISHED |
| Object | 21 |
—
|
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: 21 | Statement: [Spanish missions in California, numberOfMissions, 21]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMissions Context triple: [Spanish missions in California, numberOfMissions, 21]
-
A.
numberOfMemberStateMissions
Indicates the total count of missions associated with or undertaken by the member states in a given context.
-
B.
hasMissionActivity
Indicates that an entity is associated with, performs, or is involved in a specific mission-related activity.
-
C.
mission
Indicates that an entity is assigned or engaged in a specific task, operation, or purpose-directed undertaking.
-
D.
numberOfColonies
Indicates the count of distinct colonies associated with or possessed by a given entity.
-
E.
numberOfEvents
Indicates the quantity or count of events associated with a given entity or context.
- 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_69a25374990081909766d30c79a18e0e |
completed | Feb. 28, 2026, 2:31 a.m. |
| NER | Named-entity recognition | batch_69a258fd278481908ad4498e03f38e2f |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a25669d99481908c5e82ba8641205a |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a258b30f6c8190be2181f30c40e04d |
completed | Feb. 28, 2026, 2:53 a.m. |
Created at: Feb. 28, 2026, 2:39 a.m.