Triple
T8312649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Towcester |
E194626
|
entity |
| Predicate | hasRomanOrigin |
P1614
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Towcester, hasRomanOrigin, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanOrigin Context triple: [Towcester, hasRomanOrigin, true]
-
A.
hasRomanName
Indicates that an entity is associated with or known by a name derived from or used in ancient Rome.
-
B.
hasHistoricalOrigin
chosen
Indicates that something originated, was first established, or came into existence during a specific historical period or context.
-
C.
hasRomanEquivalent
Indicates that one entity corresponds to or is equivalent to another entity within the context of Roman culture, naming, or classification.
-
D.
usesRomanNumerals
Indicates that something represents numbers or sequences using the Roman numeral system rather than standard Arabic digits.
-
E.
hasLanguageOfOrigin
Indicates that one entity has its origin or source in the language specified by another 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_69ca82e6e2648190a31eaf6f4f757b2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f5173c881909f2e84d53ea33a98 |
completed | March 31, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69cb70bb3a708190bc705222092da614 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:54 p.m.