Triple
T107948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valley Forge winter encampment |
E2179
|
entity |
| Predicate | numberOfCampFollowers |
P2307
|
FINISHED |
| Object | several hundred to a few thousand |
—
|
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: several hundred to a few thousand | Statement: [Valley Forge winter encampment, numberOfCampFollowers, several hundred to a few thousand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCampFollowers Context triple: [Valley Forge winter encampment, numberOfCampFollowers, several hundred to a few thousand]
-
A.
numberOfParticipants
Indicates the total count of entities involved in a particular event, activity, or relationship.
-
B.
numberOfPersons
chosen
Indicates the total count of individual persons associated with or involved in a given entity, event, or context.
-
C.
numberOfColonies
Indicates the count of distinct colonies associated with or possessed by a given entity.
-
D.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
-
E.
numberOfChambers
Indicates the count of distinct chambers or compartments associated with an entity.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25a1199ac8190ac65ffaaf45b4f5b |
completed | Feb. 28, 2026, 2:59 a.m. |
| PD | Predicate disambiguation | batch_69a2563e7188819091e9a94e071991d7 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.