Triple
T36420449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Victoria Cross to Guy Gibson |
E897138
|
entity |
| Predicate | serviceNumberOfRecipient |
P194287
|
FINISHED |
| Object | 39404 |
—
|
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: 39404 | Statement: [Victoria Cross to Guy Gibson, serviceNumberOfRecipient, 39404]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serviceNumberOfRecipient Context triple: [Victoria Cross to Guy Gibson, serviceNumberOfRecipient, 39404]
-
A.
totalRecipients
Indicates the total number of distinct entities that receive something in the context of the described relationship or action.
-
B.
numberOfReceivers
Indicates the quantity of distinct receivers associated with or involved in a given entity, event, or transaction.
-
C.
numberOfRecipientsPerMonth
Indicates the quantity of recipients associated with an entity for each month.
-
D.
estimatedNumberOfBeneficiaries
Indicates the approximate count of individuals or entities expected to receive benefits from something.
-
E.
serviceNumberApproximate
Indicates that one entity’s service number is approximately equal to, but not necessarily exactly the same as, another entity’s service number.
- 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_69f76e559b10819099d6655a6e14587c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd68abf52881909c5a390c362b7c59 |
completed | May 8, 2026, 4:38 a.m. |
| PD | Predicate disambiguation | batch_69fd6812d0c88190930d8fa2d4b92490 |
completed | May 8, 2026, 4:35 a.m. |
| PDg | Predicate description generation | batch_69fd68ab21a0819096bfc4a8c14851ad |
completed | May 8, 2026, 4:38 a.m. |
Created at: May 3, 2026, 4:10 p.m.