Triple
T2102831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strange Meeting |
E37128
|
entity |
| Predicate | speaker2 |
P35843
|
FINISHED |
| Object | enemy soldier |
—
|
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: enemy soldier | Statement: [Strange Meeting, speaker2, enemy soldier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: speaker2 Context triple: [Strange Meeting, speaker2, enemy soldier]
-
A.
speakerType
Indicates the role or category of a participant in a communicative act (e.g., narrator, quoted speaker, system voice) within a given context.
-
B.
typicalSpeaker
Indicates that the subject is a prototypical or characteristic speaker or source of utterances in the context of the object.
-
C.
spokenOn
Indicates that an utterance or speech act occurred at or during a specific time or date.
-
D.
commonsSpeaker
Indicates that a person serves as the Speaker (presiding officer) of the House of Commons.
-
E.
spokenAlong
Indicates that two or more languages are used concurrently or within the same context in a particular place, time, or situation.
- 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_69a8861828948190924aa30c08806b3a |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abbabe1e9081908ea66c5406e2f1d9 |
completed | March 7, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_69abb7b7b6288190afa11b4d93bd5666 |
completed | March 7, 2026, 5:29 a.m. |
| PDg | Predicate description generation | batch_69abb9ce3ff08190a9501f8bb821c01c |
completed | March 7, 2026, 5:38 a.m. |
Created at: March 4, 2026, 7:43 p.m.