Triple
T4201139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Infantry of the Line |
E86066
|
entity |
| Predicate | languageOfTerminology |
P35123
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Infantry of the Line, languageOfTerminology, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfTerminology Context triple: [Infantry of the Line, languageOfTerminology, English]
-
A.
languageTerm
Indicates that one entity is a linguistic expression (word, phrase, or term) used to denote or label the other entity.
-
B.
officialTermLanguage
chosen
Indicates the language in which an official term is formally expressed or defined.
-
C.
terminologyNote
Indicates that there is an explanatory note or comment clarifying the use, meaning, or nuances of a specific term in the relationship.
-
D.
typicalLanguages
Indicates the languages that are commonly or characteristically used, spoken, or associated with a given entity.
-
E.
languageName
Indicates the specific name assigned to a language in the relationship.
- 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_69aed93b89f48190a31f6d57c760e42f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af037da30481908106b27a88d59140 |
completed | March 9, 2026, 5:29 p.m. |
| PD | Predicate disambiguation | batch_69af01959c4881909eb1adcb3bdadbe6 |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:49 p.m.