Triple
T11949259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bradford City stadium fire |
E284385
|
entity |
| Predicate | standAge |
P102428
|
FINISHED |
| Object | built in 1911 |
—
|
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: built in 1911 | Statement: [Bradford City stadium fire, standAge, built in 1911]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: standAge Context triple: [Bradford City stadium fire, standAge, built in 1911]
-
A.
ageStatus
Indicates the relationship between an entity and its classification into an age-related category or status (e.g., minor, adult, senior).
-
B.
containsAge
Indicates that one entity includes or specifies the age value or age-related information of another entity.
-
C.
numberOfAges
Indicates the count of distinct ages associated with an entity or within a specified group or context.
-
D.
typicalAge
Indicates the usual or characteristic age associated with an entity, event, or condition.
-
E.
ageAtIntroduction
Indicates the age an entity had at the time it was first introduced or presented in a given context.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90346825c8190ab4482a1fc8eed56 |
completed | April 10, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3e48e08190b2fee43af4f57323 |
completed | April 10, 2026, 8:56 a.m. |
| PDg | Predicate description generation | batch_69d8dd0ba0f88190b7d5e358c27ca184 |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 8, 2026, 9:45 p.m.