Triple
T10073435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hundred Regiments Offensive |
E213684
|
entity |
| Predicate | numberOfRegimentsInvolved |
P91944
|
FINISHED |
| Object | over 100 |
—
|
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: over 100 | Statement: [Hundred Regiments Offensive, numberOfRegimentsInvolved, over 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRegimentsInvolved Context triple: [Hundred Regiments Offensive, numberOfRegimentsInvolved, over 100]
-
A.
numberOfBattalions
Indicates the quantitative relationship specifying how many battalions are associated with a given entity or context.
-
B.
numberOfTroopsInvolved
Indicates the quantity of military personnel participating in or assigned to a specific operation, event, or engagement.
-
C.
regimentalCategory
Indicates the classification or type of regiment to which a military unit or formation belongs.
-
D.
engagedForces
Indicates that one force has actively committed or deployed its military units against another force in combat or operational interaction.
-
E.
typeOfTroops
Indicates the specific category or kind of military forces involved in or associated with an entity or event.
- 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_69ca839add308190b57d53b4ec21f2d0 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd015ad488190aee3a2bfb58fb855 |
completed | April 2, 2026, 2:10 a.m. |
| PD | Predicate disambiguation | batch_69cd4b97870481908f7a89df10d58a9e |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd4f8d9b888190b8067bd916dae773 |
completed | April 1, 2026, 5:02 p.m. |
Created at: March 30, 2026, 8:59 p.m.